Application of machine learning in groundwater quality modeling - A comprehensive review

•Reviewed more than 200 papers that used machine learning in groundwater quality modeling.•Neural networks are the most used machine learning model in groundwater quality modeling.•Nitrate is the most modeled contaminants.•Suggestions for further works are proposed. Groundwater is a crucial resource...

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Bibliographische Detailangaben
Veröffentlicht in:Water research (Oxford) Jg. 233; S. 119745
Hauptverfasser: Haggerty, Ryan, Sun, Jianxin, Yu, Hongfeng, Li, Yusong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 15.04.2023
Schlagworte:
Artificial Intelligence
Deep learning
Environmental Monitoring - methods
Groundwater
groundwater contamination
Groundwater quality
Iran
Machine Learning
Neural Networks, Computer
nitrates
prediction
water quality
Iran
Deep learning
Ai
Groundwater quality
Machine learning
ISSN:0043-1354, 1879-2448, 1879-2448
Online-Zugang:Volltext
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Abstract •Reviewed more than 200 papers that used machine learning in groundwater quality modeling.•Neural networks are the most used machine learning model in groundwater quality modeling.•Nitrate is the most modeled contaminants.•Suggestions for further works are proposed. Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.
AbstractList Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.
Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.
•Reviewed more than 200 papers that used machine learning in groundwater quality modeling.•Neural networks are the most used machine learning model in groundwater quality modeling.•Nitrate is the most modeled contaminants.•Suggestions for further works are proposed. Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.
ArticleNumber 119745
Author Li, Yusong
Haggerty, Ryan
Sun, Jianxin
Yu, Hongfeng
Author_xml – sequence: 1
  givenname: Ryan
  orcidid: 0000-0002-5889-1323
  surname: Haggerty
  fullname: Haggerty, Ryan
  organization: Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
– sequence: 2
  givenname: Jianxin
  surname: Sun
  fullname: Sun, Jianxin
  organization: School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
– sequence: 3
  givenname: Hongfeng
  surname: Yu
  fullname: Yu, Hongfeng
  organization: School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
– sequence: 4
  givenname: Yusong
  orcidid: 0000-0003-1761-7907
  surname: Li
  fullname: Li, Yusong
  email: yli7@unl.edu
  organization: Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
BackLink https://www.ncbi.nlm.nih.gov/pubmed/36812816$$D View this record in MEDLINE/PubMed
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Cites_doi 10.2166/aqua.2019.064
10.1016/j.jhydrol.2013.08.038
10.4249/scholarpedia.1568
10.1007/s11356-021-17064-7
10.1002/hyp.7143
10.1016/j.mex.2019.04.027
10.3389/frai.2020.00004
10.1016/j.gsd.2021.100643
10.1038/nature14539
10.4067/S0718-58392014000300013
10.1007/s11356-021-17084-3
10.1016/j.jhydrol.2018.12.031
10.1007/s40808-016-0080-3
10.1016/j.jhydrol.2020.125321
10.1016/j.scitotenv.2019.03.045
10.1016/j.proeng.2016.07.527
10.1007/s12145-015-0222-6
10.1007/s11269-016-1440-5
10.1016/j.scitotenv.2019.135877
10.1007/s00254-007-0705-y
10.1007/s10661-018-7013-8
10.1186/s40201-016-0254-y
10.2166/hydro.2013.008
10.1038/323533a0
10.1021/es702859e
10.2166/wpt.2021.120
10.3390/toxics9110273
10.1016/j.scitotenv.2020.141107
10.1029/2018WR022643
10.1007/s00271-003-0090-6
10.1007/s11356-021-16158-6
10.1016/j.ecolind.2017.06.009
10.1016/j.scitotenv.2004.11.019
10.1016/j.scitotenv.2014.01.001
10.1080/09715010.2017.1408036
10.1016/bs.adcom.2020.07.001
10.1007/s12517-013-1042-y
10.1016/j.ecoenv.2021.113061
10.2166/hydro.2017.224
10.1016/j.scitotenv.2018.11.022
10.1111/gwat.12963
10.1080/08839514.2018.1506970
10.1007/s12665-011-1134-5
10.1007/s00477-012-0676-8
10.1016/B978-0-12-818172-0.00040-2
10.1007/s10666-020-09731-9
10.1002/clen.201400267
10.1007/BF00994018
10.1016/0165-0114(94)90242-9
10.1007/s10040-018-1900-5
10.1088/1748-9326/ac10e0
10.1109/5.537105
10.2166/hydro.2015.143
10.1007/s10668-019-00319-2
10.1111/j.1745-6584.2001.tb02311.x
10.1061/(ASCE)1084-0699(2000)5:2(162)
10.1016/0165-0114(94)00282-C
10.2134/jeq1996.00472425002500030007x
10.1016/j.gsd.2017.12.012
10.1007/BF00337288
10.1016/j.jhydrol.2013.09.048
10.1016/j.scitotenv.2019.06.320
10.2166/wst.1998.0285
10.1016/j.gexplo.2019.05.006
10.1016/j.jenvman.2004.10.006
10.1029/2018WR024638
10.1016/j.jconhyd.2017.11.002
10.1002/2016WR020197
10.1109/72.159061
10.1007/s12665-017-6990-1
10.1016/j.ecolind.2021.107790
10.5194/hess-20-2353-2016
10.1016/j.jhydrol.2018.05.003
10.1016/j.jocs.2020.101104
10.1007/s11269-018-2147-6
10.2166/hydro.2012.075
10.2166/aqua.2019.062
10.1016/j.jhydrol.2021.126370
10.1007/s12517-020-06257-y
10.1021/acs.est.0c06740
10.1007/s12665-019-8092-8
10.1016/j.jhydrol.2020.125351
10.1007/s10661-014-3650-8
10.1016/S0019-9958(65)90241-X
10.1007/s10040-009-0451-1
10.1007/s10661-006-1497-3
10.1016/j.jhydrol.2004.11.010
10.1016/j.watres.2019.04.054
10.21203/rs.3.rs-1028294/v1
10.1162/neco.1989.1.3.295
10.1007/s00477-015-1088-3
10.1016/j.scitotenv.2020.136511
10.1007/s11831-020-09513-2
10.1016/j.watres.2011.08.010
10.1155/2014/458329
10.1016/j.jhydrol.2017.06.019
10.1007/s10666-015-9468-0
10.1016/j.jhydrol.2010.12.016
10.1007/s12517-020-05904-8
10.1007/s10661-012-2802-y
10.1111/gwat.13063
10.1021/acs.est.8b01679
10.1007/s11749-016-0481-7
10.1007/s12205-016-0572-8
10.1007/s10661-013-3353-6
10.1016/j.agwat.2020.106625
10.1021/es0113854
10.1021/es202875e
10.1016/0165-0114(94)90297-6
10.1007/s11631-022-00553-y
10.1016/j.neunet.2013.01.008
10.1016/j.mcm.2010.11.027
10.2166/nh.2016.072
10.1016/S0167-9473(01)00065-2
10.31127/tuje.1032314
10.1016/j.jhydrol.2019.04.039
10.2166/aqua.2017.035
10.1007/s10661-020-08695-3
10.2166/hydro.2011.072
10.1016/j.envsoft.2017.06.043
10.1080/19443994.2015.1049411
10.15244/pjoes/140170
10.1007/s00254-006-0237-x
10.1109/JSEN.2014.2347996
10.1007/s00254-007-1136-5
10.1002/2017WR021470
10.1016/j.jhydrol.2019.124200
10.1016/j.envsoft.2004.05.001
10.1007/s12665-014-3784-6
10.1007/s40808-017-0290-3
10.1007/s11269-018-1971-z
10.1134/S0097807822030162
10.1002/2013WR015037
10.1016/S1462-0758(01)00045-0
10.1007/s11356-017-8489-4
10.2134/jeq1997.00472425002600030035x
10.1016/j.jhydrol.2017.03.002
10.1016/j.jhydrol.2015.10.025
10.1016/S0952-1976(03)00054-X
10.1016/j.scitotenv.2018.07.054
10.1029/2017WR021749
10.1029/93WR03511
10.2166/hydro.2010.064
10.1016/j.jenvman.2016.09.082
10.1007/978-3-031-01548-9
10.1016/B978-0-323-89861-4.00027-0
10.1007/s00477-016-1338-z
10.1016/j.ecolmodel.2004.07.021
10.1007/s11269-021-02969-2
10.1016/j.jhydrol.2020.124989
10.1016/j.chemosphere.2021.130265
10.1007/s12665-017-6938-5
10.1016/j.neunet.2019.01.012
10.1088/1748-9326/ab7d5c
10.1021/acs.est.0c05239
10.1016/j.scitotenv.2017.12.152
10.1002/tqem.21775
10.1016/j.cageo.2005.07.003
10.1016/j.jconhyd.2018.10.010
10.1007/s13201-016-0508-y
10.1007/s12517-012-0570-1
10.1016/j.scitotenv.2021.151065
10.1016/j.scitotenv.2017.05.192
10.1029/2018WR023106
10.3390/hydrology6010024
10.1016/S0378-3774(03)00159-8
10.2166/wh.2022.015
10.1007/s12517-017-2867-6
10.1016/j.gexplo.2013.12.001
10.1109/MCAS.2006.1688199
10.1109/TIT.1982.1056489
10.1007/s11356-020-11319-5
10.1007/s12517-013-1179-8
10.1016/j.jhazmat.2007.01.119
10.17221/46/2010-SWR
10.1007/s00128-018-2406-5
10.1016/j.cam.2020.112982
10.1016/j.scitotenv.2017.11.185
10.1007/s12665-021-10147-1
10.1109/TNN.2009.2015974
10.1016/j.scitotenv.2015.07.080
10.1109/ACCESS.2019.2912200
10.1080/15275920490495873
10.1029/2020WR028207
10.1016/j.jenvman.2018.08.019
10.1016/j.jhydrol.2009.07.039
10.1016/j.ecolmodel.2005.08.045
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References Malakar, Sarkar, Mukherjee, Bhanja, Sun (bib0133) 2021
(Vol. 122, pp. 1–21). Elsevier.
Ramasamy, Krishnan, Ritter, Bernard (bib0185) 2003
Emmert-Streib, Yang, Feng, Tripathi, Dehmer (bib0065) 2020; 3
Lee, Yun, Yu, Kim, Lee, Lee (bib0123) 2019; 569
Liu, Gu, Li, Carlson (bib0128) 2020; 380
Nadiri, Sedghi, Khatibi, Sadeghfam (bib0154) 2018; 227
Du, Zhao, Lei (bib0059) 2017; 552
Hu, Huang, Chan (bib0090) 2003; 16
Barzegar, Asghari Moghaddam, Adamowski, Fijani (bib0027) 2017; 31
Yang (bib0247) 2020; 46
Nor, Faramarzi, Yunus, Ibrahim (bib0164) 2015; 15
Cho, Sthiannopkao, Pachepsky, Kim, Kim (bib0045) 2011; 45
Elbeltagi, Pande, Kouadri, Islam (bib0062) 2022; 29
Charulatha, Srinivasalu, Uma Maheswari, Venugopal, Giridharan (bib0042) 2017; 10
Huang, Xu, Liu, Liu, Wang (bib0091) 2011; 54
Kohonen, Oja, Simula, Visa, Kangas (bib0115) 1996; 84
Wang, Huang, Saratchandran, Sundararajan (bib0237) 2005; 5
Awais, Aslam, Maqsoom, Khalil, Ullah, Azam, Imran (bib0020) 2021
Al-Mahallawi, Mania, Hani, Shahrour (bib0005) 2012; 65
Yu, Cui, Sreekanth, Mangeon, Doble, Xin, Rassam, Gilfedder (bib0250) 2020; 590
LeCun, Bengio, Hinton (bib0122) 2015; 521
Podgorski, Labhasetwar, Saha, Berg (bib0177) 2018; 52
.
Sheikh Khozani, Iranmehr, Wan Mohtar (bib0207) 2022; 0
Nolan, Hitt, Ruddy (bib0162) 2002; 36
Samek, Montavon, Vedaldi, Hansen, Müller (bib0202) 2019
Kleene (bib0110) 2016
Agrawal, Sinha, Kumar, Agarwal, Banerjee, Villuri, Annavarapu, Dwivedi, Dera, Sinha, Pasupuleti (bib0001) 2021
Opitz, Maclin (bib0168) 1999; 11
Kohonen, Honkela (bib0114) 2007; 2
Barzegar, Razzagh, Quilty, Adamowski, Kheyrollah Pour, Booij (bib0030) 2021; 598
Nourani, Alami, Vousoughi (bib0166) 2015; 18
Hong, Rosen (bib0087) 2001; 3
Liu, Ming, Ankumah (bib0126) 2005; 346
Nadiri, Gharekhani, Khatibi, Moghaddam (bib0152) 2017; 24
Khashei-Siuki, Sarbazi (bib0104) 2015; 8
Kisi, Azad, Kashi, Saeedian, Hashemi, Ghorbani (bib0108) 2019; 33
Sakizadeh (bib0201) 2016; 2
Muhammetoglu, Yardimci (bib0149) 2006; 118
Soriano, Siegel, Johnson, Gutchess, Xiong, Li, Clark, Plata, Deziel, Saiers (bib0216) 2021; 16
Teso, Poe, Younglove, McCool (bib0221) 1996; 25
Quinlan (bib0180) 1992; 92
Aguilera, Fernández, Ropero, Molina (bib0002) 2013; 27
Debnath, Shah (bib0055) 2002
Tesoriero, Gronberg, Juckem, Miller, Austin (bib0222) 2017; 53
Wu, Alvareda, Polya, Blanco, Gamazo (bib0243) 2021
Yang, Zhang, Hou, Lei, Tai, Chen, Chen (bib0246) 2017; 19
Chapelle, Scholkopf, Zien (bib0041) 2009; 20
Beerala, Gobinath, Shyamala, Manvitha (bib0032) 2019; 7
Zhu, Goldberg (bib0257) 2009; 3
Kheradpisheh, Talebi, Rafati, Ghaneian, Ehrampoush (bib0105) 2015; 20
Singh, Datta (bib0212) 2004; 5
Rokach, Maimon (bib0192) 2005
Alexandrov, Vesselinov (bib0009) 2014; 50
Kumar, Pati (bib0118) 2022; 63
Cortes, Vapnik (bib0049) 1995; 20
Heidarzadeh (bib0085) 2017; 66
Rokhshad, Khashei Siuki, Yaghoobzadeh (bib0193) 2021; 14
Buckley, Hayashi (bib0038) 1994; 66
Dixon (bib0057) 2001
Park, Ligaray, Kim, Kim, Cho, Sthiannopkao (bib0173) 2016; 57
Water, Initiative, Programme (bib0238) 2016
Li, Yoder, Odhiambo, Zhang (bib0124) 2004; 23
Yang, Zhou, Tang (bib0245) 2006
Raheja, Goel, Pal (bib0182) 2022; 17
Kumar, Pati (bib0117) 2022; 20
Kaluli, Madramootoo, Djebbar (bib0097) 1998; 38
Nolan, Clark (bib0160) 1997; 26
Sahoo, Ray, Wade (bib0198) 2005; 183
El Bilali, Taleb, Brouziyne (bib0061) 2021; 245
Knoll, Breuer, Bach (bib0112) 2020; 15
Baghapour, Fadaei Nobandegani, Talebbeydokhti, Bagherzadeh, Nadiri, Gharekhani, Chitsazan (bib0022) 2016; 14
Anning, Paul, McKinney, Huntington, Bexfield, Thiros (bib0015) 2012
Selmane, Dougha, Hasbaia, Ferhati, Redjem (bib0204) 2022; 41
MacQueen (bib0131) 1967; 1
Kouadri, Pande, Panneerselvam, Moharir, Elbeltagi (bib0116) 2022; 29
Gharib, Davies (bib0076) 2021; 152
Sunayana, Kalawapudi, Dube, Sharma (bib0218) 2020; 22
Tiyasha, Tung, Yaseen (bib0224) 2020; 585
Bedi, Samal, Ray, Snow (bib0031) 2020; 192
Jalalkamali (bib0094) 2015; 8
Wagh, Panaskar, Muley, Mukate, Gaikwad (bib0233) 2018; 7
Yegnanarayana (bib0248) 2009
Najib, Asghari Moghaddam, Allah Nadiri, Fijani (bib0156) 2022; 8
Wang, Liu, Wu, Bao, Liu (bib0236) 2006; 50
Wilson, Close, Abraham, Sarris, Banasiak, Stenger, Hadfield (bib0240) 2020; 705
Rahmati, Choubin, Fathabadi, Coulon, Soltani, Shahabi, Mollaefar, Tiefenbacher, Cipullo, Ahmad, Tien Bui (bib0184) 2019; 688
Vadiati, Asghari-Moghaddam, Nakhaei, Adamowski, Akbarzadeh (bib0230) 2016; 184
Rizeei, Azeez, Pradhan, Khamees (bib0189) 2018; 190
Aryafar, Khosravi, Zarepourfard, Rooki (bib0018) 2019; 78
Darwishe, Khattabi, Chaaban, Louche, Masson, Carlier (bib0051) 2017; 76
Anjum, R., Ali, S.A., & Siddiqui, M.A. (2021).
Vesselinov, Alexandrov, O'Malley (bib0231) 2018; 212
Zare, Bayat, Daneshkare (bib0253) 2011; 25
Sahour, Gholami, Vazifedan (bib0199) 2020; 591
Zounemat-Kermani, Mahdavi-Meymand, Fadaee, Batelaan, Hinkelmann (bib0259) 2022; 31
Li, Ye, Du (bib0125) 2022
Khaki, Yusoff, Islami (bib0100) 2015; 43
Wang, Oldham, Hipsey (bib0235) 2016; 154
Ehteshami, Farahani, Tavassoli (bib0060) 2016; 2
Lombard, Bryan, Jones, Bulka, Bradley, Backer, Focazio, Silverman, Toccalino, Argos, Gribble, Ayotte (bib0130) 2021; 55
Maroufpoor, Fakheri-Fard, Shiri (bib0136) 2019; 25
Rodriguez-Galiano, Mendes, Garcia-Soldado, Chica-Olmo, Ribeiro (bib0191) 2014; 476–477
Shekofteh, Afyuni, Hajabbasi, Iversen, Nezamabadi-pour, Abassi, Sheikholeslam (bib0208) 2012; 15
Asadi, Hosseini, Ataie-Ashtiani, Simmons (bib0019) 2017; 96
Chittaranjan, K (bib0044) 2000; 5
Katoch, Chauhan, Kumar (bib0098) 2021
Dixon (bib0056) 2005; 309
Chui (bib0047) 1992
Fuentes, Casanova, Seguel, NÃ!`jera, Salazar (bib0074) 2014; 74
Modrogan, Diaconu, Orbulet, Miron (bib0142) 2010; 61
De Jesus, Senoro, Dela Cruz, Chan (bib0053) 2021; 9
Gupta, N. (2021). Chapter One - Introduction to hardware accelerator systems for artificial intelligence and machine learning. In S. Kim & G. C. Deka (Eds.)
Zhao, Luk, Niu, Shi, Wang (bib0254) 2017
Mettu, Latifi (bib0139) 2021
Rumelhart, Hinton, Williams (bib0195) 1986; 323
Najafzadeh, Homaei, Mohamadi (bib0155) 2022; 29
Zadeh (bib0251) 1965; 8
Alexandridis, Zapranis (bib0008) 2013; 42
Khalil, Almasri, McKee, Kaluarachchi (bib0102) 2005
Almasri, Kaluarachchi (bib0011) 2005; 20
WorldBank. (n.d.).
Mosaffa, Sadeghi, Mallakpour, Naghdyzadegan Jahromi, Pourghasemi (bib0145) 2022
Nolan (bib0159) 2001; 39
Gemitzi, Petalas, Pisinaras, Tsihrintzis (bib0075) 2009; 23
Ostad-Ali-Askari, Shayannejad, Ghorbanizadeh-Kharazi (bib0169) 2017; 21
Shen (bib0209) 2018; 54
Nadiri, Norouzi, Khatibi, Gharekhani (bib0153) 2019; 574
Elzain, Chung, Senapathi, Sekar, Lee, Roy, Hassan, Sabarathinam (bib0064) 2022; 229
MoasheriP0F, AbadiP4F (bib0141) 2012
Nadiri, Fijani, Tsai, Asghari Moghaddam (bib0150) 2013; 15
Khalaj, Kholghi, Saghafian, Bazrafshan (bib0101) 2019; 68
Ni, Yao, Song, Hua (bib0158) 2022; 31
Hastie, Tibshirani, Friedman (bib0083) 2009
Isazadeh, Biazar, Ashrafzadeh (bib0092) 2017; 76
Tran, Tsujimura, Ha, Nguyen, Binh, Dang, Doan, Bui, Anh Ngoc, Phu, Thuc, Pham (bib0226) 2021; 127
Senoro, de Jesus, Mendoza, Apostol, Escalona, Chan (bib0205) 2022
Gholami, Khaleghi, Sebghati (bib0079) 2017; 7
Nourani, Andalib, Dąbrowska (bib0167) 2017; 548
Taşan, Taşan, Demir (bib0220) 2022
Akansu, Haddad (bib0004) 1992
Ransom, Katherine, Nolan, A. Traum, Faunt, Bell, Gronberg, Wheeler, Z. Rosecrans, Jurgens, Schwarz, Belitz, M. Eberts, Kourakos, Harter (bib0187) 2017; 601–602
Kumari, Pathak, Prakash (bib0119) 2016
Erickson, Elliott, Christenson, Krall (bib0066) 2018; 54
Choi, Yun, Kim, Kim, Kim, Koh (bib0046) 2014; 137
Nolan, Fienen, Lorenz (bib0161) 2015; 531
Rahman, Kono, Hosono (bib0183) 2022; 846
Halgamuge, Glesner (bib0082) 1994; 65
Chakraborty, Sarkar, Mukherjee, Shamsudduha, Ahmed, Bhattacharya, Mitra (bib0040) 2020; 748
Hinton, Sejnowski (bib0086) 1999
Shrestha, Mahmood (bib0210) 2019; 7
The USGS implemented the National,to water-quality management and.
Al-Mukhtar, Al-Yaseen (bib0006) 2019; 6
LAAFOU, OMARI, Abdelaziz (bib0120) 2016; 1
Jebastina, Prince Arulraj (bib0095) 2018; 101
Che Nordin, Mohd, Koting, Ismail, Sherif, El-Shafie (bib0043) 2021; 14
Barzegar, Moghaddam, Deo, Fijani, Tziritis (bib0029) 2018; 621
Liu, Liu, Fu, Li, Imran, Cui, Abrar (bib0127) 2017; 81
Fabbrocino, Rainieri, Paduano, Ricciardi (bib0069) 2019; 204
Shrivastava, Sahu, Jhariya (bib0211) 2022; 2022
Norouzi, Moghaddam (bib0165) 2020; 13
Ritzel, Eheart, Ranjithan (bib0188) 1994; 30
Maiti, Erram, Gupta, Tiwari, Kulkarni, Sangpal (bib0132) 2013; 185
Maria, Foddis, Montisci, Uras, Matzeu, Seddaiu, Carletti (bib0134) 2022
Lambora, Gupta, Chopra (bib0121) 2019
Mohammadi, Ghaderpoori, Yousefi, Rahmatipoor, Javan (bib0144) 2016; 3
Ouedraogo, Defourny, Vanclooster (bib0171) 2019; 27
Haghbin, Sharafati, Dixon, Kumar (bib0081) 2021; 28
Haykin, Network (bib0084) 2004; 2
Sadeghfam, Hassanzadeh, Nadiri, Zarghami (bib0196) 2016; 30
Trabelsi, Ali (bib0225) 2022
Mosavi, Sajedi Hosseini, Choubin, Taromideh, Ghodsi, Nazari, Dineva (bib0147) 2021; 28
Rumelhart, Hinton, Williams (bib0194) 1985
Azimi, Azhdary Moghaddam, Hashemi Monfared (bib0021) 2019; 220
Buckley, Yoichi (bib0039) 1995; 71
Kılıçaslan, Tuna, Gezer, Gulez, Arkoc, Potirakis (bib0109) 2014; 10
Boy-Roura, Nolan, Menció, Mas-Pla (bib0037) 2013; 505
Friedel, Wilson, Close, Buscema, Abraham, Banasiak (bib0072) 2020; 580
Sedghi, Nadiri (bib0203) 2022; 12
Tutmez, Hatipoglu, Kaymak (bib0227) 2006; 32
Podgorski, Araya, Berg (bib0176) 2022; 833
Wang, Hipsey, Ahmed, Oldham (bib0234) 2018; 54
Singha, Pasupuleti, Singha, Singh, Kumar (bib0213) 2021; 276
Hosseini, Mahjouri (bib0089) 2014; 186
Khan, Liaqat, Mohamed (bib0103) 2021; 0
Aris, Praveena, Abdullah, Radojevic (bib0017) 2011; 14
Erickson, Elliott, Brown, Stackelberg, Ransom, Reddy, Cravotta (bib0068) 2021; 55
Yesilnacar, Sahinkaya, Naz, Ozkaya (bib0249) 2008; 56
Kihumba, Longo, Vanclooster (bib0107) 2015
Ransom, Nolan, Stackelberg, Belitz, Fram (bib0186) 2022; 807
Cintula, Fermüller, Noguera (bib0048) 2021
Elhatip, Kömür (bib0063) 2008; 53
Keskin, Düğenci, Kaçaroğlu (bib0099) 2015; 73
Berenji, Khedkar (bib0033) 1992; 3
Bindal, Singh (bib0035) 2019; 159
Dahiya, Singh, Gaur, Garg, Kushwaha (bib0050) 2007; 147
Zheng, Maier, Wu, Dandy, Gupta, Zhang (bib0255) 2018; 54
Sajedi-Hos
Nadiri (10.1016/j.watres.2023.119745_bib0153) 2019; 574
Arabgol (10.1016/j.watres.2023.119745_bib0016) 2016; 21
De Jesus (10.1016/j.watres.2023.119745_bib0053) 2021; 9
Fuentes (10.1016/j.watres.2023.119745_bib0074) 2014; 74
Agrawal (10.1016/j.watres.2023.119745_bib0001) 2021
Trabelsi (10.1016/j.watres.2023.119745_bib0225) 2022
10.1016/j.watres.2023.119745_bib0228
Kılıçaslan (10.1016/j.watres.2023.119745_bib0109) 2014; 10
Liu (10.1016/j.watres.2023.119745_bib0128) 2020; 380
Nolan (10.1016/j.watres.2023.119745_bib0161) 2015; 531
Mohammadi (10.1016/j.watres.2023.119745_bib0144) 2016; 3
Chittaranjan (10.1016/j.watres.2023.119745_bib0044) 2000; 5
Jafari (10.1016/j.watres.2023.119745_bib0093) 2019; 68
Beerala (10.1016/j.watres.2023.119745_bib0032) 2019; 7
Wilson (10.1016/j.watres.2023.119745_bib0240) 2020; 705
Khalaj (10.1016/j.watres.2023.119745_bib0101) 2019; 68
Dixon (10.1016/j.watres.2023.119745_bib0057) 2001
Markus (10.1016/j.watres.2023.119745_bib0135) 2010; 12
Gemitzi (10.1016/j.watres.2023.119745_bib0075) 2009; 23
Nakagawa (10.1016/j.watres.2023.119745_bib0157) 2016; 48
Alexandridis (10.1016/j.watres.2023.119745_bib0008) 2013; 42
Nolan (10.1016/j.watres.2023.119745_bib0162) 2002; 36
Sadeghfam (10.1016/j.watres.2023.119745_bib0196) 2016; 30
Polikar (10.1016/j.watres.2023.119745_bib0178) 2006; 6
Hu (10.1016/j.watres.2023.119745_bib0090) 2003; 16
Charulatha (10.1016/j.watres.2023.119745_bib0042) 2017; 10
Najafzadeh (10.1016/j.watres.2023.119745_bib0155) 2022; 29
Keskin (10.1016/j.watres.2023.119745_bib0099) 2015; 73
Opitz (10.1016/j.watres.2023.119745_bib0168) 1999; 11
Norouzi (10.1016/j.watres.2023.119745_bib0165) 2020; 13
Wagh (10.1016/j.watres.2023.119745_bib0233) 2018; 7
Al-Mukhtar (10.1016/j.watres.2023.119745_bib0006) 2019; 6
Judeh (10.1016/j.watres.2023.119745_bib0096) 2022; 49
Selmane (10.1016/j.watres.2023.119745_bib0204) 2022; 41
Kumari (10.1016/j.watres.2023.119745_bib0119) 2016
Lambora (10.1016/j.watres.2023.119745_bib0121) 2019
Ostad-Ali-Askari (10.1016/j.watres.2023.119745_bib0169) 2017; 21
Kohonen (10.1016/j.watres.2023.119745_bib0113) 1982; 43
Ni (10.1016/j.watres.2023.119745_bib0158) 2022; 31
Baghapour (10.1016/j.watres.2023.119745_bib0022) 2016; 14
Ritzel (10.1016/j.watres.2023.119745_bib0188) 1994; 30
Hospedales (10.1016/j.watres.2023.119745_bib0088) 2022; 44
Wang (10.1016/j.watres.2023.119745_bib0237) 2005; 5
Banerjee (10.1016/j.watres.2023.119745_bib0024) 2011; 398
Debnath (10.1016/j.watres.2023.119745_bib0055) 2002
Aguilera (10.1016/j.watres.2023.119745_bib0002) 2013; 27
de Menezes (10.1016/j.watres.2023.119745_bib0054) 2020; 712
Taşan (10.1016/j.watres.2023.119745_bib0220) 2022
An (10.1016/j.watres.2023.119745_bib0013) 2022
Tiyasha, Tung (10.1016/j.watres.2023.119745_bib0223) 2020; 585
10.1016/j.watres.2023.119745_bib0241
Halgamuge (10.1016/j.watres.2023.119745_bib0082) 1994; 65
Dixon (10.1016/j.watres.2023.119745_bib0058) 2009; 17
Isazadeh (10.1016/j.watres.2023.119745_bib0092) 2017; 76
Barzegar (10.1016/j.watres.2023.119745_bib0026) 2016; 2
Wang (10.1016/j.watres.2023.119745_bib0235) 2016; 154
Fijani (10.1016/j.watres.2023.119745_bib0070) 2013; 503
Sze (10.1016/j.watres.2023.119745_bib0219) 2017
Friedman (10.1016/j.watres.2023.119745_bib0073) 2002; 38
Mattern (10.1016/j.watres.2023.119745_bib0137) 2009; 376
Alagha (10.1016/j.watres.2023.119745_bib0007) 2014; 186
Du (10.1016/j.watres.2023.119745_bib0059) 2017; 552
Ouedraogo (10.1016/j.watres.2023.119745_bib0171) 2019; 27
Zheng (10.1016/j.watres.2023.119745_bib0255) 2018; 54
Ehteshami (10.1016/j.watres.2023.119745_bib0060) 2016; 2
Kohonen (10.1016/j.watres.2023.119745_bib0114) 2007; 2
Lloyd (10.1016/j.watres.2023.119745_bib0129) 1982; 28
Wagh (10.1016/j.watres.2023.119745_bib0232) 2017; 3
Mosavi (10.1016/j.watres.2023.119745_bib0146) 2020
Modrogan (10.1016/j.watres.2023.119745_bib0142) 2010; 61
Yang (10.1016/j.watres.2023.119745_bib0245) 2006
Friedel (10.1016/j.watres.2023.119745_bib0072) 2020; 580
MacQueen (10.1016/j.watres.2023.119745_bib0131) 1967; 1
Vadiati (10.1016/j.watres.2023.119745_bib0230) 2016; 184
Khalil (10.1016/j.watres.2023.119745_bib0102) 2005
Fabbrocino (10.1016/j.watres.2023.119745_bib0069) 2019; 204
Samek (10.1016/j.watres.2023.119745_bib0202) 2019
El Bilali (10.1016/j.watres.2023.119745_bib0061) 2021; 245
Yesilnacar (10.1016/j.watres.2023.119745_bib0249) 2008; 56
Kheradpisheh (10.1016/j.watres.2023.119745_bib0105) 2015; 20
Zounemat-Kermani (10.1016/j.watres.2023.119745_bib0259) 2022; 31
Maria (10.1016/j.watres.2023.119745_bib0134) 2022
Nadiri (10.1016/j.watres.2023.119745_bib0152) 2017; 24
MoasheriP0F (10.1016/j.watres.2023.119745_bib0141) 2012
Wheeler (10.1016/j.watres.2023.119745_bib0239) 2015; 536
Singh (10.1016/j.watres.2023.119745_bib0212) 2004; 5
Knoll (10.1016/j.watres.2023.119745_bib0112) 2020; 15
Saghebian (10.1016/j.watres.2023.119745_bib0197) 2014; 7
Almasri (10.1016/j.watres.2023.119745_bib0010) 2005; 74
Sajedi-Hosseini (10.1016/j.watres.2023.119745_bib0200) 2018; 644
Yang (10.1016/j.watres.2023.119745_bib0246) 2017; 19
Parisi (10.1016/j.watres.2023.119745_bib0172) 2019; 113
Messier (10.1016/j.watres.2023.119745_bib0138) 2019; 655
10.1016/j.watres.2023.119745_bib0014
Akakuru (10.1016/j.watres.2023.119745_bib0003) 2022
Jebastina (10.1016/j.watres.2023.119745_bib0095) 2018; 101
Nourani (10.1016/j.watres.2023.119745_bib0167) 2017; 548
Barzegar (10.1016/j.watres.2023.119745_bib0028) 2016; 30
Nadiri (10.1016/j.watres.2023.119745_bib0151) 2018; 32
Chui (10.1016/j.watres.2023.119745_bib0047) 1992
Quilty (10.1016/j.watres.2023.119745_bib0179) 2018; 563
LAAFOU (10.1016/j.watres.2023.119745_bib0120) 2016; 1
Nolan (10.1016/j.watres.2023.119745_bib0160) 1997; 26
Barlow (10.1016/j.watres.2023.119745_bib0025) 1989; 1
Bindal (10.1016/j.watres.2023.119745_bib0035) 2019; 159
Ransom (10.1016/j.watres.2023.119745_bib0186) 2022; 807
MOGARAJU (10.1016/j.watres.2023.119745_bib0143) 2022; 7
Khan (10.1016/j.watres.2023.119745_bib0103) 2021; 0
Soriano (10.1016/j.watres.2023.119745_bib0216) 2021; 16
Che Nordin (10.1016/j.watres.2023.119745_bib0043) 2021; 14
Erickson (10.1016/j.watres.2023.119745_bib0067) 2021; 57
Boser (10.1016/j.watres.2023.119745_bib0036) 1992
Zhao (10.1016/j.watres.2023.119745_bib0254) 2017
Sedghi (10.1016/j.watres.2023.119745_bib0203) 2022; 12
Zaqoot (10.1016/j.watres.2023.119745_bib0252) 2018; 32
Rodriguez-Galiano (10.1016/j.watres.2023.119745_bib0191) 2014; 476–477
Zadeh (10.1016/j.watres.2023.119745_bib0251) 1965; 8
Katoch (10.1016/j.watres.2023.119745_bib0098) 2021
Shrestha (10.1016/j.watres.2023.119745_bib0210) 2019; 7
Senoro (10.1016/j.watres.2023.119745_bib0205) 2022
Sahoo (10.1016/j.watres.2023.119745_bib0198) 2005; 183
Mosavi (10.1016/j.watres.2023.119745_bib0147) 2021; 28
Vesselinov (10.1016/j.watres.2023.119745_bib0231) 2018; 212
Anning (10.1016/j.watres.2023.119745_bib0015) 2012
Emmert-Streib (10.1016/j.watres.2023.119745_bib0065) 2020; 3
Mo (10.1016/j.watres.2023.119745_bib0140) 2019; 55
Sahour (10.1016/j.watres.2023.119745_bib0199) 2020; 591
Cintula (10.1016/j.watres.2023.119745_bib0048) 2021
Barzegar (10.1016/j.watres.2023.119745_bib0027) 2017; 31
Khashei-Siuki (10.1016/j.watres.2023.119745_bib0104) 2015; 8
Nadiri (10.1016/j.watres.2023.119745_bib0150) 2013; 15
Kisi (10.1016/j.watres.2023.119745_bib0108) 2019; 33
Awais (10.1016/j.watres.2023.119745_bib0020) 2021
Choi (10.1016/j.watres.2023.119745_bib0046) 2014; 137
Gholami (10.1016/j.watres.2023.119745_bib0078) 2022; 36
Aryafar (10.1016/j.watres.2023.119745_bib0018) 2019; 78
Elhatip (10.1016/j.watres.2023.119745_bib0063) 2008; 53
Shekofteh (10.1016/j.watres.2023.119745_bib0208) 2012; 15
Gholami (10.1016/j.watres.2023.119745_bib0079) 2017; 7
Nourani (10.1016/j.watres.2023.119745_bib0166) 2015; 18
Shrivastava (10.1016/j.watres.2023.119745_bib0211) 2022; 2022
Heidarzadeh (10.1016/j.watres.2023.119745_bib0085) 2017; 66
Li (10.1016/j.watres.2023.119745_bib0125) 2022
Nadiri (10.1016/j.watres.2023.119745_bib0154) 2018; 227
Raheja (10.1016/j.watres.2023.119745_bib0182) 2022; 17
Boy-Roura (10.1016/j.watres.2023.119745_bib0037) 2013; 505
Maiti (10.1016/j.watres.2023.119745_bib0132) 2013; 185
Nor (10.1016/j.watres.2023.119745_bib0164) 2015; 15
Kumar (10.1016/j.watres.2023.119745_bib0117) 2022; 20
Gholami (10.1016/j.watres.2023.119745_bib0077) 2022; 360
Khaki (10.1016/j.watres.2023.119745_bib0100) 2015; 43
Sheikh Khozani (10.1016/j.watres.2023.119745_bib0207) 2022; 0
Nolan (10.1016/j.watres.2023.119745_bib0163) 2012; 46
Erickson (10.1016/j.watres.2023.119745_bib0068) 2021; 55
Najib (10.1016/j.watres.2023.119745_bib0156) 2022; 8
Daubechies (10.1016/j.watres.2023.119745_bib0052) 1992
Ramasamy (10.1016/j.watres.2023.119745_bib0185) 2003
Park (10.1016/j.watres.2023.119745_bib0173) 2016; 57
Yu (10.1016/j.watres.2023.119745_bib0250) 2020; 590
Tesoriero (10.1016/j.watres.2023.119745_bib0222) 2017; 53
Kumar (10.1016/j.watres.2023.119745_bib0118) 2022; 63
10.1016/j.watres.2023.119745_bib0080
Chapelle (10.1016/j.watres.2023.119745_bib0041) 2009; 20
Biau (10.1016/j.watres.2023.119745_bib0034) 2016; 25
Singha (10.1016/j.watres.2023.119745_bib0213) 2021; 276
Singha (10.1016/j.watres.2023.119745_bib0214) 2022; 81
Dixon (10.1016/j.watres.2023.119745_bib0056) 2005; 309
Dahiya (10.1016/j.watres.2023.119745_bib0050) 2007; 147
Tiyasha, Tung (10.1016/j.watres.2023.119745_bib0224) 2020; 585
Kohonen (10.1016/j.watres.2023.119745_bib0115) 1996; 84
Kouadri (10.1016/j.watres.2023.119745_bib0116) 2022; 29
Haghbin (10.1016/j.watres.2023.119745_bib0081) 2021; 28
Zhong (10.1016/j.watres.2023.119745_bib0256) 2022; 309
Li (10.1016/j.watres.2023.119745_bib0124) 2004; 23
Lee (10.1016/j.watres.2023.119745_bib0123) 2019; 569
Ouedraogo (10.1016/j.watres.2023.119745_bib0170) 2016; 20
Sunayana, Kalawapudi (10.1016/j.watres.2023.119745_bib0218) 2020; 22
Cortes (10.1016/j.watres.2023.119745_bib0049) 1995; 20
Al-Mahallawi (10.1016/j.watres.2023.119745_bib0005) 2012; 65
Elbeltagi (10.1016/j.watres.2023.119745_bib0062) 2022; 29
Buckley (10.1016/j.watres.2023.119745_bib0039) 1995; 71
Knoll (10.1016/j.watres.2023.119745_bib0111) 2019; 668
Sirat (10.1016/j.watres.2023.119745_bib0215) 2013; 6
Barzegar (10.1016/j.watres.2023.119745_bib0030) 2021; 598
Cho (10.1016/j.watres.2023.119745_b
References_xml – volume: 186
  start-page: 3685
  year: 2014
  end-page: 3699
  ident: bib0089
  article-title: Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater
  publication-title: Environ. Monit. Assess
– volume: 68
  start-page: 573
  year: 2019
  end-page: 584
  ident: bib0093
  article-title: Prediction of groundwater quality parameter in the Tabriz plain, Iran using soft computing methods
  publication-title: J. Water Supply
– volume: 74
  start-page: 365
  year: 2005
  end-page: 381
  ident: bib0010
  article-title: Multi-criteria decision analysis for the optimal management of nitrate contamination of aquifers
  publication-title: J. Environ. Manage.
– volume: 20
  start-page: 829
  year: 2022
  end-page: 848
  ident: bib0117
  article-title: Assessment of groundwater arsenic contamination using machine learning in Varanasi, Uttar Pradesh, India
  publication-title: J. Water Heal.
– volume: 46
  year: 2020
  ident: bib0247
  article-title: Nature-inspired optimization algorithms: challenges and open problems
  publication-title: J. Comput. Sci.
– volume: 2
  start-page: 1568
  year: 2007
  ident: bib0114
  article-title: Kohonen network
  publication-title: Scholarpedia
– volume: 32
  start-page: 727
  year: 2018
  end-page: 744
  ident: bib0252
  article-title: A comparative study of ann for predicting nitrate concentration in groundwater wells in the southern area of gaza strip
  publication-title: Appl. Artif. Intell.
– volume: 229
  year: 2022
  ident: bib0064
  article-title: Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination
  publication-title: Ecotoxicol. Environ. Saf.
– volume: 536
  start-page: 481
  year: 2015
  end-page: 488
  ident: bib0239
  article-title: Modeling groundwater nitrate concentrations in private wells in Iowa
  publication-title: Sci. Total Environ.
– year: 2022
  ident: bib0003
  article-title: Integrating machine learning and multi-linear regression modeling approaches in groundwater quality assessment around Obosi, SE Nigeria
  publication-title: Environ., Develop. Sustainab.
– volume: 624
  start-page: 661
  year: 2018
  end-page: 672
  ident: bib0190
  article-title: Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods
  publication-title: Sci. Total Environ.
– volume: 5
  start-page: 3127
  year: 2005
  end-page: 3132
  ident: bib0237
  article-title: Time series study of GGAP-RBF network: predictions of Nasdaq stock and nitrate contamination of drinking water
– volume: 24
  start-page: 8562
  year: 2017
  end-page: 8577
  ident: bib0152
  article-title: Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models
  publication-title: Environmen. Sci. Pollut. Res.
– volume: 33
  start-page: 847
  year: 2019
  end-page: 861
  ident: bib0108
  article-title: Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods
  publication-title: Water Resour. Manage.
– volume: 44
  start-page: 5149
  year: 2022
  end-page: 5169
  ident: bib0088
  article-title: Meta-learning in neural networks: a survey
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– volume: 30
  start-page: 4585
  year: 2016
  end-page: 4601
  ident: bib0196
  article-title: Localization of Groundwater Vulnerability Assessment Using Catastrophe Theory
  publication-title: Water Resour. Manage.
– volume: 3
  start-page: 36
  year: 2017
  ident: bib0232
  article-title: Estimation of nitrate concentration in groundwater of Kadava river basin-Nashik district, Maharashtra, India by using artificial neural network model
  publication-title: Model. Earth Syst. Environ.
– volume: 54
  start-page: 4785
  year: 2018
  end-page: 4804
  ident: bib0234
  article-title: The Impact of Landscape Characteristics on Groundwater Dissolved Organic Nitrogen: insights From Machine Learning Methods and Sensitivity Analysis
  publication-title: Water Resour Res
– volume: 3
  year: 2016
  ident: bib0144
  article-title: Prediction and modeling of fluoride concentrations in groundwater resources using an artificial neural network: a case study in Khaf
  publication-title: Environmen. Heal. Engin. Manag. J.
– volume: 245
  year: 2021
  ident: bib0061
  article-title: Groundwater quality forecasting using machine learning algorithms for irrigation purposes
  publication-title: Agric. Water Manage.
– volume: 705
  year: 2020
  ident: bib0240
  article-title: Achieving unbiased predictions of national-scale groundwater redox conditions via data oversampling and statistical learning
  publication-title: Sci. Total Environ.
– volume: 43
  start-page: 59
  year: 1982
  end-page: 69
  ident: bib0113
  article-title: Self-organized formation of topologically correct feature maps
  publication-title: Biol. Cybern.
– volume: 6
  start-page: 21
  year: 2006
  end-page: 45
  ident: bib0178
  article-title: Ensemble based systems in decision making
  publication-title: IEEE Circuits Syst. Mag.
– volume: 7
  start-page: 43
  year: 2019
  end-page: 58
  ident: bib0032
  article-title: Water Quality Prediction Using Statistical Tool and Machine Learning Algorithm
  publication-title: Int. J. Chemoinform. Chem. Engin.
– volume: 7
  start-page: 53040
  year: 2019
  end-page: 53065
  ident: bib0210
  article-title: Review of deep learning algorithms and architectures
  publication-title: IEEE Access
– start-page: 1893
  year: 2017
  end-page: 1899
  ident: bib0242
  article-title: Parameter estimation of nonlinear nitrate prediction model using genetic algorithm
  publication-title: 2017 IEEE Congress on Evolutionary Computation (CEC)
– volume: 45
  start-page: 5535
  year: 2011
  end-page: 5544
  ident: bib0045
  article-title: Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network
  publication-title: Water Res.
– volume: 212
  start-page: 134
  year: 2018
  end-page: 142
  ident: bib0231
  article-title: Contaminant source identification using semi-supervised machine learning
  publication-title: J. Contam. Hydrol.
– year: 1999
  ident: bib0086
  article-title: Unsupervised learning: Foundations of Neural Computation
– volume: 12
  start-page: 58
  year: 2022
  end-page: 74
  ident: bib0203
  publication-title: Evaluation of Groundwater Resources in Varzeqan City By Introducing Unsupervised Combination Method to Determine the Categories of Drinkability
– start-page: 14
  year: 2022
  ident: bib0225
  article-title: Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia
  publication-title: Sustainability (Switzerland)
– volume: 10
  year: 2008
  ident: bib0244
  publication-title: Clustering
– volume: 9
  year: 2021
  ident: bib0053
  article-title: A hybrid neural network–particle swarm optimization informed spatial interpolation technique for groundwater quality mapping in a small island province of the Philippines
  publication-title: Toxics
– volume: 1
  start-page: 295
  year: 1989
  end-page: 311
  ident: bib0025
  article-title: Unsupervised learning
  publication-title: Neural Comput
– volume: 57
  year: 2021
  ident: bib0067
  article-title: Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States
  publication-title: Water Resour. Res.
– volume: 7
  start-page: 4767
  year: 2014
  end-page: 4777
  ident: bib0197
  article-title: Ground water quality classification by decision tree method in Ardebil region, Iran
  publication-title: Arabian J. Geosci.
– volume: 65
  start-page: 917
  year: 2012
  end-page: 928
  ident: bib0005
  article-title: Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas
  publication-title: Environ. Earth Sci.
– volume: 655
  start-page: 512
  year: 2019
  end-page: 519
  ident: bib0138
  article-title: Modeling groundwater nitrate exposure in private wells of North Carolina for the agricultural health study
  publication-title: Sci. Total Environ.
– volume: 580
  year: 2020
  ident: bib0072
  article-title: Comparison of four learning-based methods for predicting groundwater redox status
  publication-title: J. Hydrol. (Amst)
– volume: 28
  start-page: 129
  year: 1982
  end-page: 137
  ident: bib0129
  article-title: Least squares quantization in PCM
  publication-title: IEEE Trans. Inf. Theory
– volume: 14
  start-page: 13
  year: 2016
  ident: bib0022
  article-title: Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran
  publication-title: J. Environmen. Heal. Sci. Engin.
– reference: U.S.G.S. (n.d.). About the Program. In
– volume: 38
  start-page: 127
  year: 1998
  end-page: 134
  ident: bib0097
  article-title: Modeling nitrate leaching using neural networks
  publication-title: Water Sci. Technol.
– volume: 220
  start-page: 6
  year: 2019
  end-page: 17
  ident: bib0021
  article-title: Prediction of annual drinking water quality reduction based on Groundwater Resource Index using the artificial neural network and fuzzy clustering
  publication-title: J. Contam. Hydrol.
– year: 2002
  ident: bib0055
  article-title: Wavelet Transforms and Their Applications
– volume: 57
  start-page: 12227
  year: 2016
  end-page: 12236
  ident: bib0173
  article-title: Development of enhanced groundwater arsenic prediction model using machine learning approaches in Southeast Asian countries
  publication-title: Desalination Water Treat
– volume: 55
  start-page: 5791
  year: 2021
  end-page: 5805
  ident: bib0068
  article-title: Machine-Learning Predictions of High Arsenic and High Manganese at Drinking Water Depths of the Glacial Aquifer System, Northern Continental United States
  publication-title: Environ. Sci. Technol.
– volume: 84
  start-page: 1358
  year: 1996
  end-page: 1384
  ident: bib0115
  article-title: Engineering applications of the self-organizing map
  publication-title: Proc. IEEE
– volume: 14
  year: 2021
  ident: bib0043
  article-title: Groundwater quality forecasting modelling using artificial intelligence: a review
  publication-title: Groundwater Sustain. Develop.
– start-page: 321
  year: 2005
  end-page: 352
  ident: bib0192
  article-title: Clustering methods
  publication-title: Data Mining and Knowledge Discovery Handbook
– volume: 21
  start-page: 134
  year: 2017
  end-page: 140
  ident: bib0169
  article-title: Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran
  publication-title: KSCE J. Civ. Eng.
– volume: 505
  start-page: 150
  year: 2013
  end-page: 162
  ident: bib0037
  article-title: Regression model for aquifer vulnerability assessment of nitrate pollution in the Osona region (NE Spain)
  publication-title: J. Hydrol. (Amst)
– volume: 748
  year: 2020
  ident: bib0040
  article-title: Modeling regional-scale groundwater arsenic hazard in the transboundary Ganges River Delta, India and Bangladesh: infusing physically-based model with machine learning
  publication-title: Sci. Total Environ.
– volume: 43
  start-page: 551
  year: 2015
  end-page: 560
  ident: bib0100
  article-title: Application of the artificial neural network and neuro-fuzzy system for assessment of groundwater quality
  publication-title: CLEAN – Soil, Air, Water
– start-page: 14
  year: 2022
  ident: bib0125
  article-title: Imputation of Ammonium Nitrogen Concentration in Groundwater Based on a Machine Learning Method
  publication-title: Water (Switzerland)
– volume: 14
  start-page: 206
  year: 2011
  end-page: 220
  ident: bib0017
  article-title: Statistical approaches and hydrochemical modelling of groundwater system in a small tropical island
  publication-title: J. Hydroinf.
– volume: 11
  start-page: 169
  year: 1999
  end-page: 198
  ident: bib0168
  article-title: Popular Ensemble Methods: an Empirical Study
  publication-title: J. Artif. Int. Res.
– year: 1992
  ident: bib0004
  article-title: Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
– volume: 50
  start-page: 637
  year: 2006
  end-page: 644
  ident: bib0236
  article-title: Prediction of agriculture derived groundwater nitrate distribution in North China Plain with GIS-based BPNN
  publication-title: Environ. Geol.
– volume: 227
  start-page: 415
  year: 2018
  end-page: 428
  ident: bib0154
  article-title: Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks
  publication-title: J. Environ. Manage.
– volume: 23
  start-page: 29
  year: 2004
  end-page: 37
  ident: bib0124
  article-title: Simulation of nitrate distribution under drip irrigation using artificial neural networks
  publication-title: Irrigat. Sci.
– volume: 15
  start-page: 1474
  year: 2013
  end-page: 1490
  ident: bib0150
  article-title: Supervised committee machine with artificial intelligence for prediction of fluoride concentration
  publication-title: J. Hydroinf.
– volume: 30
  start-page: 883
  year: 2016
  end-page: 899
  ident: bib0028
  article-title: A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran
  publication-title: Stochastic Environmen. Res. Risk Assess.
– volume: 66
  start-page: 86
  year: 2017
  end-page: 95
  ident: bib0085
  article-title: A practical low-cost model for prediction of the groundwater quality using artificial neural networks
  publication-title: J. Water Supply
– year: 2022
  ident: bib0134
  article-title: Prediction of Nitrate Concentration in Groundwater Using an Artificial Neural Network (ANN) Approach
– volume: 3
  start-page: 724
  year: 1992
  end-page: 740
  ident: bib0033
  article-title: Learning and tuning fuzzy logic controllers through reinforcements
  publication-title: IEEE Trans. Neural Networks
– volume: 6
  year: 2019
  ident: bib0006
  article-title: Modeling Water Quality Parameters Using Data-Driven Models, a Case Study Abu-Ziriq Marsh in South of Iraq
  publication-title: Hydrology
– volume: 154
  start-page: 1176
  year: 2016
  end-page: 1184
  ident: bib0235
  article-title: Comparison of machine learning techniques and variables for groundwater dissolved organic nitrogen prediction in an Urban Area
  publication-title: Procedia Eng
– volume: 65
  start-page: 1
  year: 1994
  end-page: 12
  ident: bib0082
  article-title: Neural networks in designing fuzzy systems for real world applications
  publication-title: Fuzzy Sets Syst.
– year: 1992
  ident: bib0047
  article-title: An Introduction to Wavelets
– volume: 48
  start-page: 840
  year: 2016
  end-page: 850
  ident: bib0157
  article-title: Classification of groundwater chemistry in Shimabara, using self-organizing maps
  publication-title: Hydrol. Res.
– start-page: 393
  year: 2016
  end-page: 402
  ident: bib0119
  article-title: A review of application of artificial neural network in ground water modeling
  publication-title: Recent Advances in Mathematics, Statistics and Computer Science
– volume: 42
  start-page: 1
  year: 2013
  end-page: 27
  ident: bib0008
  article-title: Wavelet neural networks: a practical guide
  publication-title: Neural Networks
– volume: 41
  start-page: 893
  year: 2022
  end-page: 909
  ident: bib0204
  article-title: Hydrogeochemical processes and multivariate analysis for groundwater quality in the arid Maadher region of Hodna, northern Algeria
  publication-title: Acta Geochimica
– volume: 552
  start-page: 44
  year: 2017
  end-page: 51
  ident: bib0059
  article-title: The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series
  publication-title: J. Hydrol. (Amst)
– start-page: 1
  year: 2015
  end-page: 13
  ident: bib0107
  article-title: Modelling nitrate pollution pressure using a multivariate statistical approach: the case of Kinshasa groundwater body
  publication-title: Democr. Repub. Congo Hydrogeol. J
– start-page: 784
  year: 2006
  end-page: 793
  ident: bib0245
  article-title: A classifier ensemble method for fuzzy classifiers
  publication-title: International Conference on Fuzzy Systems and Knowledge Discovery
– volume: 92
  start-page: 343
  year: 1992
  end-page: 348
  ident: bib0180
  article-title: Learning with continuous classes
  publication-title: 5th Australian Joint Conference Artif. Intell.
– volume: 7
  start-page: 3633
  year: 2017
  end-page: 3647
  ident: bib0079
  article-title: A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)
  publication-title: Appl. Water Sci.
– volume: 476–477
  start-page: 189
  year: 2014
  end-page: 206
  ident: bib0191
  article-title: Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain)
  publication-title: Sci. Total Environ.
– volume: 53
  start-page: 1157
  year: 2008
  end-page: 1164
  ident: bib0063
  article-title: Evaluation of water quality parameters for the Mamasin dam in Aksaray City in the central Anatolian part of Turkey by means of artificial neural networks
  publication-title: Environ. Geol.
– volume: 376
  start-page: 275
  year: 2009
  end-page: 284
  ident: bib0137
  article-title: Discriminating sources of nitrate pollution in an unconfined sandy aquifer
  publication-title: J. Hydrol. (Amst)
– volume: 55
  start-page: 5012
  year: 2021
  end-page: 5023
  ident: bib0130
  article-title: Machine learning models of arsenic in private wells throughout the conterminous united states as a tool for exposure assessment in human health studies
  publication-title: Environ. Sci. Technol.
– volume: 833
  year: 2022
  ident: bib0176
  article-title: Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh – Machine learning spatial prediction modeling and comparison with arsenic
  publication-title: Sci. Total Environ.
– volume: 15
  start-page: 64004
  year: 2020
  ident: bib0112
  article-title: Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning
  publication-title: Environ. Res. Lett.
– volume: 26
  start-page: 849
  year: 1997
  end-page: 857
  ident: bib0160
  article-title: Selenium in irrigated agricultural areas of the Western United States
  publication-title: J. Environ. Qual.
– volume: 26
  start-page: 187
  year: 2021
  end-page: 203
  ident: bib0174
  article-title: Machine learning models for predicting the ammonium concentration in alluvial groundwaters
  publication-title: Environmen. Model. Assessment
– volume: 3
  start-page: 4
  year: 2020
  ident: bib0065
  article-title: An Introductory Review of Deep Learning for Prediction Models With Big Data
  publication-title: Front. Artif. Intell.
– year: 2022
  ident: bib0220
  article-title: Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods
  publication-title: Environmen. Sci. Pollut. Res.
– volume: 190
  start-page: 633
  year: 2018
  ident: bib0189
  article-title: Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models
  publication-title: Environ. Monit. Assess
– volume: 10
  start-page: 128
  year: 2017
  ident: bib0042
  article-title: Evaluation of ground water quality contaminants using linear regression and artificial neural network models
  publication-title: Arabian J. Geosci.
– volume: 76
  start-page: 649
  year: 2017
  ident: bib0051
  article-title: Prediction and control of nitrate concentrations in groundwater by implementing a model based on GIS and artificial neural networks (ANN)
  publication-title: Environ. Earth Sci.
– volume: 63
  year: 2022
  ident: bib0118
  article-title: Assessment of groundwater arsenic contamination level in Jharkhand, India using machine learning
  publication-title: J. Comput. Sci.
– volume: 20
  start-page: 851
  year: 2005
  end-page: 871
  ident: bib0011
  article-title: Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data
  publication-title: Environmen. Modell. Software
– year: 2009
  ident: bib0248
  article-title: Artificial Neural Networks
– volume: 309
  start-page: 17
  year: 2005
  end-page: 38
  ident: bib0056
  article-title: Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis
  publication-title: J. Hydrol. (Amst)
– volume: 8
  start-page: 885
  year: 2015
  end-page: 894
  ident: bib0094
  article-title: Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters
  publication-title: Earth Sci. Inform.
– volume: 846
  year: 2022
  ident: bib0183
  article-title: Self-organizing map improves understanding on the hydrochemical processes in aquifer systems
  publication-title: Sci. Total Environ.
– year: 2019
  ident: bib0202
  publication-title: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
– volume: 574
  start-page: 744
  year: 2019
  end-page: 759
  ident: bib0153
  article-title: Groundwater DRASTIC vulnerability mapping by unsupervised and supervised techniques using a modelling strategy in two levels
  publication-title: J. Hydrol. (Amst)
– volume: 601–602
  start-page: 1160
  year: 2017
  end-page: 1172
  ident: bib0187
  article-title: A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA
  publication-title: Sci. Total Environ.
– volume: 0
  start-page: 1
  year: 2022
  end-page: 18
  ident: bib0207
  article-title: Improving Water Quality Index prediction for water resources management plans in Malaysia: application of machine learning techniques
  publication-title: Geocarto Int.
– volume: 31
  start-page: 241
  year: 2022
  end-page: 251
  ident: bib0259
  article-title: Groundwater quality modeling: On the analogy between integrative PSO and MRFO mathematical and machine learning models
  publication-title: Environ. Qual. Manage.
– start-page: 144
  year: 1992
  end-page: 152
  ident: bib0036
  article-title: A Training Algorithm for Optimal Margin Classifiers
  publication-title: Proceedings of the Fifth Annual Workshop on Computational Learning Theory
– volume: 38
  start-page: 367
  year: 2002
  end-page: 378
  ident: bib0073
  article-title: Stochastic gradient boosting
  publication-title: Computat. Statist. \& Data Analy.
– volume: 23
  start-page: 372
  year: 2009
  end-page: 383
  ident: bib0075
  article-title: Spatial prediction of nitrate pollution in groundwaters using neural networks and GIS: an application to South Rhodope aquifer (Thrace, Greece)
  publication-title: Hydrol. Process
– volume: 20
  start-page: 2353
  year: 2016
  end-page: 2381
  ident: bib0170
  article-title: A meta-analysis and statistical modelling of nitrates in groundwater at the African scale
  publication-title: Hydrol. Earth Syst. Sci.
– volume: 204
  start-page: 90
  year: 2019
  end-page: 111
  ident: bib0069
  article-title: Cluster analysis for groundwater classification in multi-aquifer systems based on a novel correlation index
  publication-title: J. Geochem. Explor.
– start-page: 1
  year: 2021
  end-page: 36
  ident: bib0098
  article-title: A review on genetic algorithm: past, present, and future
  publication-title: Multimed. Tools Appl.
– year: 1985
  ident: bib0194
  article-title: Learning Internal Representations By Error Propagation
– volume: 29
  start-page: 17591
  year: 2022
  end-page: 17605
  ident: bib0062
  article-title: Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India
  publication-title: Environmen. Sci. Pollut. Res.
– volume: 56
  start-page: 19
  year: 2008
  end-page: 25
  ident: bib0249
  article-title: Neural network prediction of nitrate in groundwater of Harran Plain, Turkey
  publication-title: Environ. Geol.
– volume: 591
  year: 2020
  ident: bib0199
  article-title: A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer
  publication-title: J. Hydrol. (Amst)
– volume: 323
  start-page: 533
  year: 1986
  end-page: 536
  ident: bib0195
  article-title: Learning representations by back-propagating errors
  publication-title: Nature
– volume: 54
  start-page: 995
  year: 2011
  end-page: 1004
  ident: bib0091
  article-title: Spatial distribution pattern analysis of groundwater nitrate nitrogen pollution in Shandong intensive farming regions of China using neural network method
  publication-title: Math Comput. Model.
– start-page: 585
  year: 2022
  end-page: 591
  ident: bib0145
  article-title: Application of machine learning algorithms in hydrology
  publication-title: Comput. Earth Environmen. Sci.
– volume: 68
  start-page: 121
  year: 2019
  end-page: 135
  ident: bib0101
  article-title: Impact of climate variation and human activities on groundwater quality in northwest of Iran
  publication-title: J. Water Supply
– reference: .
– reference: Gupta, N. (2021). Chapter One - Introduction to hardware accelerator systems for artificial intelligence and machine learning. In S. Kim & G. C. Deka (Eds.),
– volume: 55
  start-page: 3856
  year: 2019
  end-page: 3881
  ident: bib0140
  article-title: Deep Autoregressive Neural Networks for High-Dimensional Inverse Problems in Groundwater Contaminant Source Identification
  publication-title: Water Resour. Res.
– year: 2012
  ident: bib0015
  article-title: Predicted Nitrate and Arsenic Concentrations in Basin-Fill Aquifers of the Southwestern United States
– volume: 25
  start-page: 232
  year: 2019
  end-page: 238
  ident: bib0136
  article-title: Study of the spatial distribution of groundwater quality using soft computing and geostatistical models
  publication-title: ISH J. Hydraul. Engin.
– volume: 101
  start-page: 403
  year: 2018
  end-page: 409
  ident: bib0095
  article-title: Spatial prediction of nitrate concentration using GIS and ANFIS modelling in groundwater
  publication-title: Bull. Environ. Contam. Toxicol.
– start-page: 12
  year: 2022
  ident: bib0205
  article-title: Groundwater Quality Monitoring Using In-Situ Measurements and Hybrid Machine Learning with Empirical Bayesian Kriging Interpolation Method
  publication-title: Appl. Sci.
– volume: 159
  start-page: 65
  year: 2019
  end-page: 76
  ident: bib0035
  article-title: Predicting groundwater arsenic contamination: regions at risk in highest populated state of India
  publication-title: Water Res.
– volume: 185
  start-page: 3445
  year: 2013
  end-page: 3465
  ident: bib0132
  article-title: Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks
  publication-title: Environ. Monit. Assess
– volume: 360
  year: 2022
  ident: bib0077
  article-title: Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran
  publication-title: J. Clean Prod.
– volume: 32
  start-page: 3023
  year: 2018
  end-page: 3040
  ident: bib0151
  article-title: Mapping aquifer vulnerability indices using artificial intelligence-running multiple frameworks (AIMF) with supervised and unsupervised learning
  publication-title: Water Resour. Manage.
– volume: 380
  year: 2020
  ident: bib0128
  article-title: Machine learning and transport simulations for groundwater anomaly detection
  publication-title: J. Comput. Appl. Math
– volume: 807
  year: 2022
  ident: bib0186
  article-title: Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States
  publication-title: Sci. Total Environ.
– volume: 548
  start-page: 170
  year: 2017
  end-page: 183
  ident: bib0167
  article-title: Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds
  publication-title: J Hydrol (Amst)
– year: 2016
  ident: bib0238
  article-title: Spring: Managing Groundwater Sustainability
– volume: 590
  year: 2020
  ident: bib0250
  article-title: Deep learning emulators for groundwater contaminant transport modelling
  publication-title: J. Hydrol. (Amst)
– volume: 31
  start-page: 1769
  year: 2022
  end-page: 1781
  ident: bib0158
  article-title: Groundwater quality evaluation based on PCA-PSO-SVM machine learning in Xinzhou City, China
  publication-title: Polish J. Environmen. Stud.
– volume: 46
  start-page: 901
  year: 2012
  end-page: 908
  ident: bib0163
  article-title: Verifiable Metamodels for Nitrate Losses to Drains and Groundwater in the Corn Belt, USA
  publication-title: Environ. Sci. Technol.
– volume: 3
  start-page: 1
  year: 2009
  end-page: 130
  ident: bib0257
  article-title: Introduction to semi-supervised learning
  publication-title: Synthesis Lectures Artif. Intell. Mach. Learn.
– reference: Anjum, R., Ali, S.A., & Siddiqui, M.A. (2021).
– volume: 27
  start-page: 1081
  year: 2019
  end-page: 1098
  ident: bib0171
  article-title: Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale
  publication-title: Hydrogeol. J.
– volume: 59
  start-page: 201
  year: 2012
  end-page: 212
  ident: bib0229
  article-title: Data-driven modelling of groundwater vulnerability to nitrate pollution in Slovenia (Podatkovno vodeno modeliranje ranljivosti podzemne vode na nitratno onesnaženje v Sloveniji)
  publication-title: RMZ—Mater. Geoenviron
– year: 2016
  ident: bib0110
  article-title: Representation of Events in Nerve Nets and Finite Automata
– volume: 20
  start-page: 273
  year: 1995
  end-page: 297
  ident: bib0049
  article-title: Support-Vector Networks
  publication-title: Mach. Learn.
– start-page: 337
  year: 2009
  end-page: 387
  ident: bib0083
  article-title: Boosting and additive trees
  publication-title: The Elements of Statistical Learning
– volume: 0
  start-page: 1
  year: 2021
  end-page: 19
  ident: bib0103
  article-title: A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers
  publication-title: Geocarto. Int.
– start-page: 545
  year: 2021
  end-page: 557
  ident: bib0133
  article-title: Use of machine learning and deep learning methods in groundwater
  publication-title: Global Groundwater
– volume: 2
  start-page: 1
  year: 2016
  end-page: 13
  ident: bib0026
  article-title: Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction
  publication-title: Modeling Earth Syst. Environ.
– volume: 73
  start-page: 5333
  year: 2015
  end-page: 5347
  ident: bib0099
  article-title: Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey)
  publication-title: Environ. Earth Sci.
– start-page: 41
  year: 2005
  ident: bib0102
  article-title: Applicability of statistical learning algorithms in groundwater quality modeling
  publication-title: Water Resour. Res.
– volume: 276
  year: 2021
  ident: bib0213
  article-title: Prediction of groundwater quality using efficient machine learning technique
  publication-title: Chemosphere
– volume: 36
  start-page: 2138
  year: 2002
  end-page: 2145
  ident: bib0162
  article-title: Probability of Nitrate Contamination of Recently Recharged Groundwaters in the Conterminous United States
  publication-title: Environ. Sci. Technol.
– volume: 8
  start-page: 903
  year: 2015
  end-page: 912
  ident: bib0104
  article-title: Evaluation of ANFIS, ANN, and geostatistical models to spatial distribution of groundwater quality (case study: mashhad plain in Iran)
  publication-title: Arabian J. Geosci.
– volume: 53
  start-page: 7316
  year: 2017
  end-page: 7331
  ident: bib0222
  article-title: Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification
  publication-title: Water Resour. Res.
– volume: 1
  start-page: 281
  year: 1967
  end-page: 297
  ident: bib0131
  article-title: Some methods for classification and analysis of multivariate observations
  publication-title: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics Probabil.
– volume: 5
  start-page: 143
  year: 2004
  end-page: 153
  ident: bib0212
  article-title: Groundwater pollution source identification and simultaneous parameter estimation using pattern matching by artificial neural network
  publication-title: Environ. Forensics
– volume: 503
  start-page: 89
  year: 2013
  end-page: 100
  ident: bib0070
  article-title: Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran
  publication-title: J. Hydrol. (Amst)
– volume: 61
  start-page: 6
  year: 2010
  ident: bib0142
  article-title: Forecasting study for nitrate ion removal using reactive barriers
  publication-title: REV. CHIM.(Bucharest)
– volume: 598
  year: 2021
  ident: bib0030
  article-title: Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models
  publication-title: J. Hydrol. (Amst)
– volume: 20
  start-page: 542
  year: 2009
  ident: bib0041
  article-title: Semi-supervised learning (chapelle, o. et al., eds.; 2006)[
  publication-title: IEEE Trans. Neural Networks
– volume: 1
  start-page: 135
  year: 2016
  end-page: 140
  ident: bib0120
  article-title: Application of artificial neural networks with error back-propagation algorithm to predict nitrate levels in water
  publication-title: Adv. Inform. Technol.
– volume: 621
  start-page: 697
  year: 2018
  end-page: 712
  ident: bib0029
  article-title: Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms
  publication-title: Sci. Total Environ.
– volume: 7
  start-page: 436
  year: 2018
  end-page: 445
  ident: bib0233
  article-title: Neural network modelling for nitrate concentration in groundwater of Kadava River basin, Nashik, Maharashtra, India
  publication-title: Groundwater Sustain. Develop.
– volume: 49
  start-page: 503
  year: 2022
  end-page: 514
  ident: bib0096
  article-title: Use of GIS, Statistics and Machine Learning for Groundwater Quality Management: application to Nitrate Contamination
  publication-title: Water Resour.
– volume: 2022
  start-page: 726
  year: 2022
  end-page: 731
  ident: bib0211
  article-title: Comparative analysis on ensemble learning techniques for groundwater quality assessment of chhattisgarh region
  publication-title: Proceedings - 2022 IEEE World Conference on Applied Intelligence and Computing, AIC
– volume: 585
  year: 2020
  ident: bib0224
  article-title: A survey on river water quality modelling using artificial intelligence models: 2000–2020
  publication-title: J. Hydrol. (Amst)
– start-page: 11
  year: 2021
  ident: bib0020
  article-title: Assessing nitrate contamination risks in groundwater: a machine learning approach
  publication-title: Appl. Sci.
– volume: 5
  start-page: 162
  year: 2000
  end-page: 171
  ident: bib0044
  article-title: Neural Networks for Agrichemical Vulnerability Assessment of Rural Private Wells
  publication-title: J. Hydrol. Eng.
– start-page: 645
  year: 2017
  end-page: 650
  ident: bib0254
  article-title: Hardware Acceleration for Machine Learning
  publication-title: 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
– volume: 58
  start-page: 723
  year: 2020
  end-page: 734
  ident: bib0106
  article-title: Stochastic Modeling of Groundwater Fluoride Contamination: introducing Lazy Learners
  publication-title: Groundwater
– volume: 12
  start-page: 251
  year: 2010
  end-page: 261
  ident: bib0135
  article-title: Prediction of weekly nitrate-N fluctuations in a small agricultural watershed in Illinois
  publication-title: J. Hydroinf.
– start-page: 12
  year: 2020
  ident: bib0146
  article-title: Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models
  publication-title: Water (Basel)
– year: 2001
  ident: bib0057
  article-title: Application of Neuro-fuzzy techniques to Predict Groundwater Vulnerability in Northwest Arkansas
– volume: 21
  start-page: 71
  year: 2016
  end-page: 82
  ident: bib0016
  article-title: Predicting Nitrate Concentration and Its Spatial Distribution in Groundwater Resources Using Support Vector Machines (SVMs) Model
  publication-title: Environmen. Model. Assess.
– volume: 644
  start-page: 954
  year: 2018
  end-page: 962
  ident: bib0200
  article-title: A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination
  publication-title: Sci. Total Environ.
– volume: 17
  start-page: 336
  year: 2022
  end-page: 351
  ident: bib0182
  article-title: Prediction of groundwater quality indices using machine learning algorithms
  publication-title: Water Pract. Technol.
– year: 2017
  ident: bib0219
  article-title: Hardware for machine learning: challenges and opportunities
  publication-title: 2017 IEEE Custom Integrated Circuits Conference (CICC)
– volume: 66
  start-page: 1
  year: 1994
  end-page: 13
  ident: bib0038
  article-title: Fuzzy neural networks: a survey
  publication-title: Fuzzy Sets Syst.
– volume: 569
  start-page: 685
  year: 2019
  end-page: 697
  ident: bib0123
  article-title: The combined use of self-organizing map technique and fuzzy c-means clustering to evaluate urban groundwater quality in Seoul metropolitan city, South Korea
  publication-title: J. Hydrol. (Amst)
– volume: 63
  start-page: 169
  year: 2003
  end-page: 183
  ident: bib0206
  article-title: Neural networks for predicting nitrate-nitrogen in drainage water
  publication-title: Agric. Water Manage.
– volume: 30
  start-page: 1589
  year: 1994
  end-page: 1603
  ident: bib0188
  article-title: Using genetic algorithms to solve a multiple objective groundwater pollution containment problem
  publication-title: Water Resour. Res.
– volume: 668
  start-page: 1317
  year: 2019
  end-page: 1327
  ident: bib0111
  article-title: Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning
  publication-title: Sci. Total Environ.
– volume: 50
  start-page: 7332
  year: 2014
  end-page: 7347
  ident: bib0009
  article-title: Blind source separation for groundwater pressure analysis based on nonnegative matrix factorization
  publication-title: Water Resour. Res.
– volume: 3
  start-page: 193
  year: 2001
  end-page: 204
  ident: bib0087
  article-title: Intelligent characterisation and diagnosis of the groundwater quality in an urban fractured-rock aquifer using an artificial neural network
  publication-title: Urban Water
– start-page: 14
  year: 2022
  ident: bib0013
  article-title: An Integrated Bayesian and Machine Learning Approach Application to Identification of Groundwater Contamination Source Parameters
  publication-title: Water (Switzerland)
– volume: 531
  start-page: 902
  year: 2015
  end-page: 911
  ident: bib0161
  article-title: A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA
  publication-title: J. Hydrol. (Amst)
– volume: 74
  start-page: 340
  year: 2014
  end-page: 348
  ident: bib0074
  article-title: Morphophysical pedotransfer functions for groundwater pollution by nitrate leaching in Central Chile
  publication-title: Chilean J. Agricul. Res.
– volume: 96
  start-page: 146
  year: 2017
  end-page: 157
  ident: bib0019
  article-title: Fuzzy vulnerability mapping of urban groundwater systems to nitrate contamination
  publication-title: Environmen. Modell. Software
– volume: 346
  start-page: 112
  year: 2005
  end-page: 120
  ident: bib0126
  article-title: Nitrate contamination in private wells in rural Alabama, United States
  publication-title: Sci. Total Environ.
– volume: 6
  start-page: 3149
  year: 2013
  end-page: 3160
  ident: bib0215
  article-title: Neural network assessment of groundwater contamination of US Mid-continent
  publication-title: Arabian J. Geosci.
– volume: 118
  start-page: 337
  year: 2006
  end-page: 354
  ident: bib0149
  article-title: A fuzzy logic approach to assess groundwater pollution levels below agricultural fields
  publication-title: Environ. Monit. Assess
– volume: 25
  start-page: 425
  year: 1996
  end-page: 432
  ident: bib0221
  article-title: Use of Logistic Regression and GIS Modeling to Predict Groundwater Vulnerability to Pesticides
  publication-title: J. Environ. Qual.
– start-page: 160
  year: 2021
  end-page: 171
  ident: bib0139
  article-title: Assessment of groundwater quality and quantity using supervised machine learning
  publication-title: Proceedings of the 27th International Conference on Systems Engineering, ICSEng 2020
– volume: 15
  start-page: 497
  year: 2015
  end-page: 504
  ident: bib0164
  article-title: Nitrate and Sulfate Estimations in Water Sources Using a Planar Electromagnetic Sensor Array and Artificial Neural Network Method
  publication-title: IEEE Sens. J.
– volume: 137
  start-page: 73
  year: 2014
  end-page: 84
  ident: bib0046
  article-title: Hydrogeochemical interpretation of South Korean groundwater monitoring data using Self-Organizing Maps
  publication-title: J. Geochem. Explor.
– volume: 52
  start-page: 9889
  year: 2018
  end-page: 9898
  ident: bib0177
  article-title: Prediction Modeling and Mapping of Groundwater Fluoride Contamination throughout India
  publication-title: Environ. Sci. Technol.
– year: 2005
  ident: bib0258
  article-title: Semi-supervised Learning Literature Survey
– year: 2021
  ident: bib0048
  article-title: Fuzzy Logic
  publication-title: ({W}inter 2)
– year: 1992
  ident: bib0052
  article-title: Ten Lectures on Wavelets
– volume: 18
  start-page: 288
  year: 2015
  end-page: 309
  ident: bib0166
  article-title: Self-organizing map clustering technique for ANN-based spatiotemporal modeling of groundwater quality parameters
  publication-title: J. Hydroinf.
– volume: 78
  start-page: 1
  year: 2019
  end-page: 13
  ident: bib0018
  article-title: Evolving genetic programming and other AI-based models for estimating groundwater quality parameters of the Khezri plain, Eastern Iran
  publication-title: Environ. Earth Sci
– volume: 32
  start-page: 421
  year: 2006
  end-page: 433
  ident: bib0227
  article-title: Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system
  publication-title: Comput. Geosci.
– volume: 29
  start-page: 21067
  year: 2022
  end-page: 21091
  ident: bib0116
  article-title: Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models
  publication-title: Environmen. Sci. Pollut. Res.
– year: 2012
  ident: bib0141
  article-title: Estimating the Groundwater Nitrate By Using Artificial Neural Network and Optimizing It By Genetic Algorithm
– volume: 398
  start-page: 212
  year: 2011
  end-page: 220
  ident: bib0024
  article-title: Artificial neural network model as a potential alternative for groundwater salinity forecasting
  publication-title: J. Hydrol. (Amst)
– volume: 7
  start-page: 64
  year: 2022
  end-page: 72
  ident: bib0143
  article-title: Application of Machine learning algorithms in investigation of groundwater quality parameters over YSR district, India
  publication-title: Turkish J. Engin.
– volume: 39
  start-page: 290
  year: 2001
  end-page: 299
  ident: bib0159
  article-title: Relating Nitrogen Sources and Aquifer Susceptibility to Nitrate in Shallow Ground Waters of the United States
  publication-title: Groundwater
– volume: 54
  year: 2018
  ident: bib0066
  article-title: Predicting geogenic Arsenic in Drinking Water Wells in Glacial Aquifers, North-Central USA: accounting for Depth-Dependent Features
  publication-title: Water Resour. Res.
– start-page: 13
  year: 2021
  ident: bib0243
  article-title: Distribution of Groundwater Arsenic in Uruguay Using Hybrid Machine Learning and Expert System Approaches
  publication-title: Water (Basel)
– volume: 585
  year: 2020
  ident: bib0223
  article-title: A survey on river water quality modelling using artificial intelligence models: 2000–2020
  publication-title: J. Hydrol. (Amst)
– volume: 183
  start-page: 29
  year: 2005
  end-page: 46
  ident: bib0198
  article-title: Pesticide prediction in ground water in North Carolina domestic wells using artificial neural networks
  publication-title: Ecol. Modell.
– volume: 28
  start-page: 10804
  year: 2021
  end-page: 10817
  ident: bib0147
  article-title: Susceptibility mapping of groundwater salinity using machine learning models
  publication-title: Environmen. Sci. Pollut. Res.
– volume: 13
  start-page: 912
  year: 2020
  ident: bib0165
  article-title: Groundwater quality assessment using random forest method based on groundwater quality indices (case study: miandoab plain aquifer, NW of Iran)
  publication-title: Arabian J. Geosci.
– volume: 19
  start-page: 784
  year: 2017
  end-page: 794
  ident: bib0246
  article-title: Shallow groundwater quality assessment: use of the improved Nemerow pollution index, wavelet transform and neural networks
  publication-title: J. Hydroinf.
– start-page: 380
  year: 2019
  end-page: 384
  ident: bib0121
  article-title: Genetic Algorithm- A Literature Review
  publication-title: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon)
– volume: 15
  start-page: 503
  year: 2012
  end-page: 515
  ident: bib0208
  article-title: Nitrate leaching from a potato field using adaptive network-based fuzzy inference system
  publication-title: J. Hydroinf.
– volume: 2
  start-page: 28
  year: 2016
  ident: bib0060
  article-title: Simulation of nitrate contamination in groundwater using artificial neural networks
  publication-title: Model. Earth Syst. Environ.
– volume: 31
  start-page: 2705
  year: 2017
  end-page: 2718
  ident: bib0027
  article-title: Comparison of machine learning models for predicting fluoride contamination in groundwater
  publication-title: Stochast. Environmen. Res. Risk Assess.
– volume: 29
  start-page: 8174
  year: 2022
  end-page: 8190
  ident: bib0155
  article-title: Reliability evaluation of groundwater quality index using data-driven models
  publication-title: Environmen. Sci. Pollut. Res.
– volume: 20
  start-page: 65
  year: 2015
  end-page: 71
  ident: bib0105
  article-title: Groundwater quality assessment using artificial neural network: a case study of Bahabad plain, Yazd, Iran
  publication-title: Desert
– volume: 36
  start-page: 451
  year: 2022
  end-page: 469
  ident: bib0078
  article-title: Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: geospatial Artificial Intelligence
  publication-title: Water Resour. Manage.
– reference: WorldBank. (n.d.).
– volume: 2
  start-page: 41
  year: 2004
  ident: bib0084
  article-title: A comprehensive foundation
  publication-title: Neural Netw.
– reference: .
– volume: 42
  start-page: 3669
  year: 2008
  end-page: 3675
  ident: bib0012
  article-title: Statistical Modeling of Global Geogenic Arsenic Contamination in Groundwater
  publication-title: Environ. Sci. Technol.
– volume: 2
  start-page: 1
  year: 2016
  end-page: 9
  ident: bib0201
  article-title: Artificial intelligence for the prediction of water quality index in groundwater systems
  publication-title: Modeling Earth Syst. Environ.
– volume: 184
  start-page: 255
  year: 2016
  end-page: 270
  ident: bib0230
  article-title: A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices
  publication-title: J. Environ. Manage.
– volume: 22
  start-page: 2801
  year: 2020
  end-page: 2816
  ident: bib0218
  article-title: Use of neural networks and spatial interpolation to predict groundwater quality
  publication-title: Environ., Develop. Sustainab.
– volume: 8
  start-page: 1061
  year: 2022
  end-page: 1080
  ident: bib0156
  article-title: Evaluating Quality Variation of Groundwater Resources in Marand Plain Using Unsupervised Combination Approach (GQI and GWQI Index)
  publication-title: Iran. J. Ecohydrol.
– volume: 563
  start-page: 336
  year: 2018
  end-page: 353
  ident: bib0179
  article-title: Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework
  publication-title: J. Hydrol. (Amst)
– volume: 27
  start-page: 435
  year: 2013
  end-page: 447
  ident: bib0002
  article-title: Groundwater quality assessment using data clustering based on hybrid Bayesian networks
  publication-title: Stochas. Environmen. Res. Risk Assess.
– volume: 54
  start-page: 8558
  year: 2018
  end-page: 8593
  ident: bib0209
  article-title: A transdisciplinary review of deep learning research and its relevance for water resources scientists
  publication-title: Water Resour. Res.
– volume: 113
  start-page: 54
  year: 2019
  end-page: 71
  ident: bib0172
  article-title: Continual lifelong learning with neural networks: a review
  publication-title: Neural. Netw.
– volume: 309
  year: 2022
  ident: bib0256
  article-title: Hydrochemical interpretation of groundwater in Yinchuan basin using self-organizing maps and hierarchical clustering
  publication-title: Chemosphere
– volume: 192
  start-page: 776
  year: 2020
  ident: bib0031
  article-title: Comparative evaluation of machine learning models for groundwater quality assessment
  publication-title: Environ. Monit. Assess.
– volume: 14
  start-page: 40
  year: 2021
  ident: bib0193
  article-title: Evaluation of a machine-based learning method to estimate the rate of nitrate penetration and groundwater contamination
  publication-title: Arabian J. Geosci.
– volume: 25
  start-page: 187
  year: 2011
  end-page: 192
  ident: bib0253
  article-title: Forecasting nitrate concentration in groundwater using artificial neural network and linear regression models
  publication-title: Int. Agrophys.
– volume: 59
  start-page: 352
  year: 2021
  end-page: 368
  ident: bib0217
  article-title: Machine learning predictions of pH in the glacial aquifer system, Northern USA
  publication-title: Groundwater
– volume: 10
  year: 2014
  ident: bib0109
  article-title: ANN-based estimation of groundwater quality using a wireless water quality network
  publication-title: Int. J. Distrib. Sens. Netw.
– volume: 7
  start-page: 73
  year: 2012
  end-page: 83
  ident: bib0148
  article-title: Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system
  publication-title: Soil Water Res.
– volume: 8
  start-page: 338
  year: 1965
  end-page: 353
  ident: bib0251
  article-title: Fuzzy sets
  publication-title: Inform. Control
– volume: 54
  start-page: 1013
  year: 2018
  end-page: 1030
  ident: bib0255
  article-title: On lack of robustness in hydrological model development due to absence of guidelines for selecting calibration and evaluation data: demonstration for data-driven models
  publication-title: Water Resour. Res.
– volume: 147
  start-page: 938
  year: 2007
  end-page: 946
  ident: bib0050
  article-title: Analysis of groundwater quality using fuzzy synthetic evaluation
  publication-title: J. Hazard. Mater.
– volume: 186
  start-page: 35
  year: 2014
  end-page: 45
  ident: bib0007
  article-title: Modeling of nitrate concentration in groundwater using artificial intelligence approach—A case study of Gaza coastal aquifer
  publication-title: Environ. Monit. Assess
– volume: 21
  start-page: 768
  year: 1965
  end-page: 769
  ident: bib0071
  article-title: Cluster analysis of multivariate data: efficiency versus interpretability of classifications
  publication-title: Biometrics
– volume: 71
  start-page: 265
  year: 1995
  end-page: 276
  ident: bib0039
  article-title: Neural nets for fuzzy systems
  publication-title: Fuzzy Sets Syst.
– volume: 712
  year: 2020
  ident: bib0054
  article-title: Modeling arsenic content in Brazilian soils: what is relevant?
  publication-title: Sci. Total Environ.
– volume: 81
  start-page: 1
  year: 2022
  end-page: 13
  ident: bib0214
  article-title: Knowledge-driven and machine learning decision tree-based approach for assessment of geospatial variation of groundwater quality around coal mining regions, Korba district, Central India
  publication-title: Environ. Earth Sci
– volume: 28
  start-page: 3569
  year: 2021
  end-page: 3591
  ident: bib0081
  article-title: Application of soft computing models for simulating nitrate contamination in groundwater: comprehensive review, assessment and future opportunities
  publication-title: Arch. Comput. Meth. Eng.
– volume: 76
  start-page: 1
  year: 2017
  end-page: 14
  ident: bib0092
  article-title: Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters
  publication-title: Environ. Earth Sci.
– volume: 17
  start-page: 1507
  year: 2009
  end-page: 1520
  ident: bib0058
  article-title: A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N
  publication-title: Hydrogeol. J.
– reference: (Vol. 122, pp. 1–21). Elsevier.
– volume: 81
  start-page: 302
  year: 2017
  end-page: 314
  ident: bib0127
  article-title: ELM evaluation model of regional groundwater quality based on the crow search algorithm
  publication-title: Ecol. Indic.
– volume: 6
  start-page: 1021
  year: 2019
  end-page: 1029
  ident: bib0181
  article-title: Protocol for the estimation of drinking water quality index (DWQI) in water resources: artificial neural network (ANFIS) and Arc-Gis
  publication-title: MethodsX
– volume: 152
  year: 2021
  ident: bib0076
  article-title: A workflow to address pitfalls and challenges in applying machine learning models to hydrology
  publication-title: Adv. Water Resour.
– volume: 16
  start-page: 131
  year: 2003
  end-page: 147
  ident: bib0090
  article-title: A fuzzy process controller for in situ groundwater bioremediation
  publication-title: Eng. Appl. Artif. Intell.
– volume: 193
  start-page: 629
  year: 2006
  end-page: 644
  ident: bib0175
  article-title: Metamodelling: theory, concepts and application to nitrate leaching modelling
  publication-title: Ecol. Modell.
– volume: 521
  start-page: 436
  year: 2015
  end-page: 444
  ident: bib0122
  article-title: Deep learning
  publication-title: Nature
– volume: 688
  start-page: 855
  year: 2019
  end-page: 866
  ident: bib0184
  article-title: Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods
  publication-title: Sci. Total Environ.
– start-page: 13
  year: 2021
  ident: bib0001
  article-title: Exploring artificial intelligence techniques for groundwater quality assessment
  publication-title: Water (Basel)
– volume: 587
  year: 2020
  ident: bib0023
  article-title: Estimation of total dissolved solids (TDS) using new hybrid machine learning models
  publication-title: J. Hydrol. (Amst)
– volume: 127
  year: 2021
  ident: bib0226
  article-title: Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
  publication-title: Ecol. Indic.
– reference: The USGS implemented the National,to water-quality management and.
– volume: 16
  start-page: 84013
  year: 2021
  ident: bib0216
  article-title: Assessment of groundwater well vulnerability to contamination through physics-informed machine learning
  publication-title: Environ. Res. Lett.
– volume: 25
  start-page: 197
  year: 2016
  end-page: 227
  ident: bib0034
  article-title: A random forest guided tour
  publication-title: test.
– year: 2003
  ident: bib0185
  article-title: Modeling Nitrate Concentration in Ground Water Using Regression and Neural Networks
– volume: 68
  start-page: 121
  issue: 2
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0101
  article-title: Impact of climate variation and human activities on groundwater quality in northwest of Iran
  publication-title: J. Water Supply
  doi: 10.2166/aqua.2019.064
– volume: 1
  start-page: 281
  issue: 14
  year: 1967
  ident: 10.1016/j.watres.2023.119745_bib0131
  article-title: Some methods for classification and analysis of multivariate observations
  publication-title: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics Probabil.
– volume: 503
  start-page: 89
  year: 2013
  ident: 10.1016/j.watres.2023.119745_bib0070
  article-title: Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2013.08.038
– volume: 2
  start-page: 1568
  issue: 1
  year: 2007
  ident: 10.1016/j.watres.2023.119745_bib0114
  article-title: Kohonen network
  publication-title: Scholarpedia
  doi: 10.4249/scholarpedia.1568
– volume: 846
  issue: July
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0183
  article-title: Self-organizing map improves understanding on the hydrochemical processes in aquifer systems
  publication-title: Sci. Total Environ.
– volume: 8
  start-page: 1061
  issue: 4
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0156
  article-title: Evaluating Quality Variation of Groundwater Resources in Marand Plain Using Unsupervised Combination Approach (GQI and GWQI Index)
  publication-title: Iran. J. Ecohydrol.
– volume: 29
  start-page: 17591
  issue: 12
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0062
  article-title: Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India
  publication-title: Environmen. Sci. Pollut. Res.
  doi: 10.1007/s11356-021-17064-7
– volume: 23
  start-page: 372
  issue: 3
  year: 2009
  ident: 10.1016/j.watres.2023.119745_bib0075
  article-title: Spatial prediction of nitrate pollution in groundwaters using neural networks and GIS: an application to South Rhodope aquifer (Thrace, Greece)
  publication-title: Hydrol. Process
  doi: 10.1002/hyp.7143
– volume: 6
  start-page: 1021
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0181
  article-title: Protocol for the estimation of drinking water quality index (DWQI) in water resources: artificial neural network (ANFIS) and Arc-Gis
  publication-title: MethodsX
  doi: 10.1016/j.mex.2019.04.027
– volume: 3
  start-page: 4
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0065
  article-title: An Introductory Review of Deep Learning for Prediction Models With Big Data
  publication-title: Front. Artif. Intell.
  doi: 10.3389/frai.2020.00004
– volume: 14
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0043
  article-title: Groundwater quality forecasting modelling using artificial intelligence: a review
  publication-title: Groundwater Sustain. Develop.
  doi: 10.1016/j.gsd.2021.100643
– volume: 521
  start-page: 436
  issue: 7553
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0122
  article-title: Deep learning
  publication-title: Nature
  doi: 10.1038/nature14539
– volume: 74
  start-page: 340
  year: 2014
  ident: 10.1016/j.watres.2023.119745_bib0074
  article-title: Morphophysical pedotransfer functions for groundwater pollution by nitrate leaching in Central Chile
  publication-title: Chilean J. Agricul. Res.
  doi: 10.4067/S0718-58392014000300013
– volume: 29
  start-page: 21067
  issue: 14
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0116
  article-title: Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models
  publication-title: Environmen. Sci. Pollut. Res.
  doi: 10.1007/s11356-021-17084-3
– volume: 569
  start-page: 685
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0123
  article-title: The combined use of self-organizing map technique and fuzzy c-means clustering to evaluate urban groundwater quality in Seoul metropolitan city, South Korea
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2018.12.031
– volume: 2
  start-page: 28
  issue: 1
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0060
  article-title: Simulation of nitrate contamination in groundwater using artificial neural networks
  publication-title: Model. Earth Syst. Environ.
  doi: 10.1007/s40808-016-0080-3
– volume: 591
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0199
  article-title: A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2020.125321
– volume: 668
  start-page: 1317
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0111
  article-title: Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2019.03.045
– volume: 154
  start-page: 1176
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0235
  article-title: Comparison of machine learning techniques and variables for groundwater dissolved organic nitrogen prediction in an Urban Area
  publication-title: Procedia Eng
  doi: 10.1016/j.proeng.2016.07.527
– ident: 10.1016/j.watres.2023.119745_bib0228
– volume: 8
  start-page: 885
  issue: 4
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0094
  article-title: Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters
  publication-title: Earth Sci. Inform.
  doi: 10.1007/s12145-015-0222-6
– volume: 30
  start-page: 4585
  issue: 13
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0196
  article-title: Localization of Groundwater Vulnerability Assessment Using Catastrophe Theory
  publication-title: Water Resour. Manage.
  doi: 10.1007/s11269-016-1440-5
– volume: 705
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0240
  article-title: Achieving unbiased predictions of national-scale groundwater redox conditions via data oversampling and statistical learning
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2019.135877
– year: 2009
  ident: 10.1016/j.watres.2023.119745_bib0248
– volume: 53
  start-page: 1157
  issue: 6
  year: 2008
  ident: 10.1016/j.watres.2023.119745_bib0063
  article-title: Evaluation of water quality parameters for the Mamasin dam in Aksaray City in the central Anatolian part of Turkey by means of artificial neural networks
  publication-title: Environ. Geol.
  doi: 10.1007/s00254-007-0705-y
– volume: 190
  start-page: 633
  issue: 11
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0189
  article-title: Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models
  publication-title: Environ. Monit. Assess
  doi: 10.1007/s10661-018-7013-8
– year: 1992
  ident: 10.1016/j.watres.2023.119745_bib0052
– volume: 14
  start-page: 13
  issue: 1
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0022
  article-title: Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran
  publication-title: J. Environmen. Heal. Sci. Engin.
  doi: 10.1186/s40201-016-0254-y
– start-page: 1
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0107
  article-title: Modelling nitrate pollution pressure using a multivariate statistical approach: the case of Kinshasa groundwater body
  publication-title: Democr. Repub. Congo Hydrogeol. J
– year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0220
  article-title: Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods
  publication-title: Environmen. Sci. Pollut. Res.
– volume: 15
  start-page: 1474
  issue: 4
  year: 2013
  ident: 10.1016/j.watres.2023.119745_bib0150
  article-title: Supervised committee machine with artificial intelligence for prediction of fluoride concentration
  publication-title: J. Hydroinf.
  doi: 10.2166/hydro.2013.008
– year: 2005
  ident: 10.1016/j.watres.2023.119745_bib0258
– year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0238
– volume: 0
  start-page: 1
  issue: 0
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0103
  article-title: A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers
  publication-title: Geocarto. Int.
– volume: 1
  start-page: 135
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0120
  article-title: Application of artificial neural networks with error back-propagation algorithm to predict nitrate levels in water
  publication-title: Adv. Inform. Technol.
– volume: 309
  issue: P2
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0256
  article-title: Hydrochemical interpretation of groundwater in Yinchuan basin using self-organizing maps and hierarchical clustering
  publication-title: Chemosphere
– volume: 11
  start-page: 169
  issue: 1
  year: 1999
  ident: 10.1016/j.watres.2023.119745_bib0168
  article-title: Popular Ensemble Methods: an Empirical Study
  publication-title: J. Artif. Int. Res.
– volume: 323
  start-page: 533
  issue: 6088
  year: 1986
  ident: 10.1016/j.watres.2023.119745_bib0195
  article-title: Learning representations by back-propagating errors
  publication-title: Nature
  doi: 10.1038/323533a0
– start-page: 337
  year: 2009
  ident: 10.1016/j.watres.2023.119745_bib0083
  article-title: Boosting and additive trees
– volume: 42
  start-page: 3669
  issue: 10
  year: 2008
  ident: 10.1016/j.watres.2023.119745_bib0012
  article-title: Statistical Modeling of Global Geogenic Arsenic Contamination in Groundwater
  publication-title: Environ. Sci. Technol.
  doi: 10.1021/es702859e
– volume: 17
  start-page: 336
  issue: 1
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0182
  article-title: Prediction of groundwater quality indices using machine learning algorithms
  publication-title: Water Pract. Technol.
  doi: 10.2166/wpt.2021.120
– start-page: 784
  year: 2006
  ident: 10.1016/j.watres.2023.119745_bib0245
  article-title: A classifier ensemble method for fuzzy classifiers
– volume: 9
  issue: 11
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0053
  article-title: A hybrid neural network–particle swarm optimization informed spatial interpolation technique for groundwater quality mapping in a small island province of the Philippines
  publication-title: Toxics
  doi: 10.3390/toxics9110273
– volume: 748
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0040
  article-title: Modeling regional-scale groundwater arsenic hazard in the transboundary Ganges River Delta, India and Bangladesh: infusing physically-based model with machine learning
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2020.141107
– volume: 54
  start-page: 8558
  issue: 11
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0209
  article-title: A transdisciplinary review of deep learning research and its relevance for water resources scientists
  publication-title: Water Resour. Res.
  doi: 10.1029/2018WR022643
– volume: 23
  start-page: 29
  issue: 1
  year: 2004
  ident: 10.1016/j.watres.2023.119745_bib0124
  article-title: Simulation of nitrate distribution under drip irrigation using artificial neural networks
  publication-title: Irrigat. Sci.
  doi: 10.1007/s00271-003-0090-6
– volume: 29
  start-page: 8174
  issue: 6
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0155
  article-title: Reliability evaluation of groundwater quality index using data-driven models
  publication-title: Environmen. Sci. Pollut. Res.
  doi: 10.1007/s11356-021-16158-6
– volume: 81
  start-page: 302
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0127
  article-title: ELM evaluation model of regional groundwater quality based on the crow search algorithm
  publication-title: Ecol. Indic.
  doi: 10.1016/j.ecolind.2017.06.009
– year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0134
– volume: 346
  start-page: 112
  issue: 1–3
  year: 2005
  ident: 10.1016/j.watres.2023.119745_bib0126
  article-title: Nitrate contamination in private wells in rural Alabama, United States
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2004.11.019
– volume: 476–477
  start-page: 189
  year: 2014
  ident: 10.1016/j.watres.2023.119745_bib0191
  article-title: Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain)
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2014.01.001
– volume: 25
  start-page: 232
  issue: 2
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0136
  article-title: Study of the spatial distribution of groundwater quality using soft computing and geostatistical models
  publication-title: ISH J. Hydraul. Engin.
  doi: 10.1080/09715010.2017.1408036
– ident: 10.1016/j.watres.2023.119745_bib0080
  doi: 10.1016/bs.adcom.2020.07.001
– volume: 7
  start-page: 4767
  issue: 11
  year: 2014
  ident: 10.1016/j.watres.2023.119745_bib0197
  article-title: Ground water quality classification by decision tree method in Ardebil region, Iran
  publication-title: Arabian J. Geosci.
  doi: 10.1007/s12517-013-1042-y
– start-page: 11
  issue: 21
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0020
  article-title: Assessing nitrate contamination risks in groundwater: a machine learning approach
  publication-title: Appl. Sci.
– volume: 229
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0064
  article-title: Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination
  publication-title: Ecotoxicol. Environ. Saf.
  doi: 10.1016/j.ecoenv.2021.113061
– volume: 19
  start-page: 784
  issue: 5
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0246
  article-title: Shallow groundwater quality assessment: use of the improved Nemerow pollution index, wavelet transform and neural networks
  publication-title: J. Hydroinf.
  doi: 10.2166/hydro.2017.224
– year: 1985
  ident: 10.1016/j.watres.2023.119745_bib0194
– volume: 655
  start-page: 512
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0138
  article-title: Modeling groundwater nitrate exposure in private wells of North Carolina for the agricultural health study
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2018.11.022
– volume: 2
  start-page: 41
  issue: 2004
  year: 2004
  ident: 10.1016/j.watres.2023.119745_bib0084
  article-title: A comprehensive foundation
  publication-title: Neural Netw.
– start-page: 1
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0098
  article-title: A review on genetic algorithm: past, present, and future
  publication-title: Multimed. Tools Appl.
– volume: 58
  start-page: 723
  issue: 5
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0106
  article-title: Stochastic Modeling of Groundwater Fluoride Contamination: introducing Lazy Learners
  publication-title: Groundwater
  doi: 10.1111/gwat.12963
– volume: 32
  start-page: 727
  issue: 7–8
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0252
  article-title: A comparative study of ann for predicting nitrate concentration in groundwater wells in the southern area of gaza strip
  publication-title: Appl. Artif. Intell.
  doi: 10.1080/08839514.2018.1506970
– volume: 25
  start-page: 187
  issue: 2
  year: 2011
  ident: 10.1016/j.watres.2023.119745_bib0253
  article-title: Forecasting nitrate concentration in groundwater using artificial neural network and linear regression models
  publication-title: Int. Agrophys.
– volume: 65
  start-page: 917
  issue: 3
  year: 2012
  ident: 10.1016/j.watres.2023.119745_bib0005
  article-title: Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-011-1134-5
– volume: 27
  start-page: 435
  issue: 2
  year: 2013
  ident: 10.1016/j.watres.2023.119745_bib0002
  article-title: Groundwater quality assessment using data clustering based on hybrid Bayesian networks
  publication-title: Stochas. Environmen. Res. Risk Assess.
  doi: 10.1007/s00477-012-0676-8
– start-page: 545
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0133
  article-title: Use of machine learning and deep learning methods in groundwater
  publication-title: Global Groundwater
  doi: 10.1016/B978-0-12-818172-0.00040-2
– volume: 26
  start-page: 187
  issue: 2
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0174
  article-title: Machine learning models for predicting the ammonium concentration in alluvial groundwaters
  publication-title: Environmen. Model. Assessment
  doi: 10.1007/s10666-020-09731-9
– volume: 43
  start-page: 551
  issue: 4
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0100
  article-title: Application of the artificial neural network and neuro-fuzzy system for assessment of groundwater quality
  publication-title: CLEAN – Soil, Air, Water
  doi: 10.1002/clen.201400267
– volume: 20
  start-page: 273
  issue: 3
  year: 1995
  ident: 10.1016/j.watres.2023.119745_bib0049
  article-title: Support-Vector Networks
  publication-title: Mach. Learn.
  doi: 10.1007/BF00994018
– start-page: 12
  issue: 10
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0146
  article-title: Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models
  publication-title: Water (Basel)
– volume: 65
  start-page: 1
  issue: 1
  year: 1994
  ident: 10.1016/j.watres.2023.119745_bib0082
  article-title: Neural networks in designing fuzzy systems for real world applications
  publication-title: Fuzzy Sets Syst.
  doi: 10.1016/0165-0114(94)90242-9
– volume: 27
  start-page: 1081
  issue: 3
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0171
  article-title: Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale
  publication-title: Hydrogeol. J.
  doi: 10.1007/s10040-018-1900-5
– volume: 16
  start-page: 84013
  issue: 8
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0216
  article-title: Assessment of groundwater well vulnerability to contamination through physics-informed machine learning
  publication-title: Environ. Res. Lett.
  doi: 10.1088/1748-9326/ac10e0
– volume: 84
  start-page: 1358
  issue: 10
  year: 1996
  ident: 10.1016/j.watres.2023.119745_bib0115
  article-title: Engineering applications of the self-organizing map
  publication-title: Proc. IEEE
  doi: 10.1109/5.537105
– volume: 10
  year: 2008
  ident: 10.1016/j.watres.2023.119745_bib0244
– volume: 18
  start-page: 288
  issue: 2
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0166
  article-title: Self-organizing map clustering technique for ANN-based spatiotemporal modeling of groundwater quality parameters
  publication-title: J. Hydroinf.
  doi: 10.2166/hydro.2015.143
– volume: 22
  start-page: 2801
  issue: 4
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0218
  article-title: Use of neural networks and spatial interpolation to predict groundwater quality
  publication-title: Environ., Develop. Sustainab.
  doi: 10.1007/s10668-019-00319-2
– volume: 39
  start-page: 290
  issue: 2
  year: 2001
  ident: 10.1016/j.watres.2023.119745_bib0159
  article-title: Relating Nitrogen Sources and Aquifer Susceptibility to Nitrate in Shallow Ground Waters of the United States
  publication-title: Groundwater
  doi: 10.1111/j.1745-6584.2001.tb02311.x
– volume: 63
  issue: July
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0118
  article-title: Assessment of groundwater arsenic contamination level in Jharkhand, India using machine learning
  publication-title: J. Comput. Sci.
– volume: 5
  start-page: 162
  issue: 2
  year: 2000
  ident: 10.1016/j.watres.2023.119745_bib0044
  article-title: Neural Networks for Agrichemical Vulnerability Assessment of Rural Private Wells
  publication-title: J. Hydrol. Eng.
  doi: 10.1061/(ASCE)1084-0699(2000)5:2(162)
– volume: 360
  issue: December 2021
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0077
  article-title: Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran
  publication-title: J. Clean Prod.
– volume: 71
  start-page: 265
  issue: 3
  year: 1995
  ident: 10.1016/j.watres.2023.119745_bib0039
  article-title: Neural nets for fuzzy systems
  publication-title: Fuzzy Sets Syst.
  doi: 10.1016/0165-0114(94)00282-C
– volume: 25
  start-page: 425
  issue: 3
  year: 1996
  ident: 10.1016/j.watres.2023.119745_bib0221
  article-title: Use of Logistic Regression and GIS Modeling to Predict Groundwater Vulnerability to Pesticides
  publication-title: J. Environ. Qual.
  doi: 10.2134/jeq1996.00472425002500030007x
– volume: 7
  start-page: 436
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0233
  article-title: Neural network modelling for nitrate concentration in groundwater of Kadava River basin, Nashik, Maharashtra, India
  publication-title: Groundwater Sustain. Develop.
  doi: 10.1016/j.gsd.2017.12.012
– volume: 43
  start-page: 59
  issue: 1
  year: 1982
  ident: 10.1016/j.watres.2023.119745_bib0113
  article-title: Self-organized formation of topologically correct feature maps
  publication-title: Biol. Cybern.
  doi: 10.1007/BF00337288
– volume: 505
  start-page: 150
  year: 2013
  ident: 10.1016/j.watres.2023.119745_bib0037
  article-title: Regression model for aquifer vulnerability assessment of nitrate pollution in the Osona region (NE Spain)
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2013.09.048
– volume: 688
  start-page: 855
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0184
  article-title: Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2019.06.320
– volume: 38
  start-page: 127
  issue: 7
  year: 1998
  ident: 10.1016/j.watres.2023.119745_bib0097
  article-title: Modeling nitrate leaching using neural networks
  publication-title: Water Sci. Technol.
  doi: 10.2166/wst.1998.0285
– volume: 204
  start-page: 90
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0069
  article-title: Cluster analysis for groundwater classification in multi-aquifer systems based on a novel correlation index
  publication-title: J. Geochem. Explor.
  doi: 10.1016/j.gexplo.2019.05.006
– year: 2001
  ident: 10.1016/j.watres.2023.119745_bib0057
– volume: 74
  start-page: 365
  issue: 4
  year: 2005
  ident: 10.1016/j.watres.2023.119745_bib0010
  article-title: Multi-criteria decision analysis for the optimal management of nitrate contamination of aquifers
  publication-title: J. Environ. Manage.
  doi: 10.1016/j.jenvman.2004.10.006
– start-page: 321
  year: 2005
  ident: 10.1016/j.watres.2023.119745_bib0192
  article-title: Clustering methods
– volume: 55
  start-page: 3856
  issue: 5
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0140
  article-title: Deep Autoregressive Neural Networks for High-Dimensional Inverse Problems in Groundwater Contaminant Source Identification
  publication-title: Water Resour. Res.
  doi: 10.1029/2018WR024638
– volume: 212
  start-page: 134
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0231
  article-title: Contaminant source identification using semi-supervised machine learning
  publication-title: J. Contam. Hydrol.
  doi: 10.1016/j.jconhyd.2017.11.002
– volume: 53
  start-page: 7316
  issue: 8
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0222
  article-title: Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification
  publication-title: Water Resour. Res.
  doi: 10.1002/2016WR020197
– volume: 3
  start-page: 724
  issue: 5
  year: 1992
  ident: 10.1016/j.watres.2023.119745_bib0033
  article-title: Learning and tuning fuzzy logic controllers through reinforcements
  publication-title: IEEE Trans. Neural Networks
  doi: 10.1109/72.159061
– start-page: 41
  issue: 5
  year: 2005
  ident: 10.1016/j.watres.2023.119745_bib0102
  article-title: Applicability of statistical learning algorithms in groundwater quality modeling
  publication-title: Water Resour. Res.
– volume: 76
  start-page: 649
  issue: 19
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0051
  article-title: Prediction and control of nitrate concentrations in groundwater by implementing a model based on GIS and artificial neural networks (ANN)
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-017-6990-1
– volume: 127
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0226
  article-title: Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
  publication-title: Ecol. Indic.
  doi: 10.1016/j.ecolind.2021.107790
– volume: 20
  start-page: 2353
  issue: 6
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0170
  article-title: A meta-analysis and statistical modelling of nitrates in groundwater at the African scale
  publication-title: Hydrol. Earth Syst. Sci.
  doi: 10.5194/hess-20-2353-2016
– volume: 563
  start-page: 336
  issue: February
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0179
  article-title: Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2018.05.003
– volume: 5
  start-page: 3127
  year: 2005
  ident: 10.1016/j.watres.2023.119745_bib0237
  article-title: Time series study of GGAP-RBF network: predictions of Nasdaq stock and nitrate contamination of drinking water
– volume: 46
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0247
  article-title: Nature-inspired optimization algorithms: challenges and open problems
  publication-title: J. Comput. Sci.
  doi: 10.1016/j.jocs.2020.101104
– volume: 585
  issue: October 2019
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0223
  article-title: A survey on river water quality modelling using artificial intelligence models: 2000–2020
  publication-title: J. Hydrol. (Amst)
– volume: 33
  start-page: 847
  issue: 2
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0108
  article-title: Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods
  publication-title: Water Resour. Manage.
  doi: 10.1007/s11269-018-2147-6
– volume: 15
  start-page: 503
  issue: 2
  year: 2012
  ident: 10.1016/j.watres.2023.119745_bib0208
  article-title: Nitrate leaching from a potato field using adaptive network-based fuzzy inference system
  publication-title: J. Hydroinf.
  doi: 10.2166/hydro.2012.075
– volume: 68
  start-page: 573
  issue: 7
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0093
  article-title: Prediction of groundwater quality parameter in the Tabriz plain, Iran using soft computing methods
  publication-title: J. Water Supply
  doi: 10.2166/aqua.2019.062
– volume: 598
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0030
  article-title: Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2021.126370
– volume: 14
  start-page: 40
  issue: 1
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0193
  article-title: Evaluation of a machine-based learning method to estimate the rate of nitrate penetration and groundwater contamination
  publication-title: Arabian J. Geosci.
  doi: 10.1007/s12517-020-06257-y
– volume: 55
  start-page: 5791
  issue: 9
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0068
  article-title: Machine-Learning Predictions of High Arsenic and High Manganese at Drinking Water Depths of the Glacial Aquifer System, Northern Continental United States
  publication-title: Environ. Sci. Technol.
  doi: 10.1021/acs.est.0c06740
– volume: 78
  start-page: 1
  issue: 3
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0018
  article-title: Evolving genetic programming and other AI-based models for estimating groundwater quality parameters of the Khezri plain, Eastern Iran
  publication-title: Environ. Earth Sci
  doi: 10.1007/s12665-019-8092-8
– volume: 590
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0250
  article-title: Deep learning emulators for groundwater contaminant transport modelling
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2020.125351
– volume: 186
  start-page: 3685
  issue: 6
  year: 2014
  ident: 10.1016/j.watres.2023.119745_bib0089
  article-title: Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater
  publication-title: Environ. Monit. Assess
  doi: 10.1007/s10661-014-3650-8
– volume: 92
  start-page: 343
  year: 1992
  ident: 10.1016/j.watres.2023.119745_bib0180
  article-title: Learning with continuous classes
  publication-title: 5th Australian Joint Conference Artif. Intell.
– start-page: 12
  issue: 1
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0205
  article-title: Groundwater Quality Monitoring Using In-Situ Measurements and Hybrid Machine Learning with Empirical Bayesian Kriging Interpolation Method
  publication-title: Appl. Sci.
– start-page: 160
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0139
  article-title: Assessment of groundwater quality and quantity using supervised machine learning
– volume: 8
  start-page: 338
  issue: 3
  year: 1965
  ident: 10.1016/j.watres.2023.119745_bib0251
  article-title: Fuzzy sets
  publication-title: Inform. Control
  doi: 10.1016/S0019-9958(65)90241-X
– volume: 17
  start-page: 1507
  issue: 6
  year: 2009
  ident: 10.1016/j.watres.2023.119745_bib0058
  article-title: A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N
  publication-title: Hydrogeol. J.
  doi: 10.1007/s10040-009-0451-1
– volume: 118
  start-page: 337
  issue: 1
  year: 2006
  ident: 10.1016/j.watres.2023.119745_bib0149
  article-title: A fuzzy logic approach to assess groundwater pollution levels below agricultural fields
  publication-title: Environ. Monit. Assess
  doi: 10.1007/s10661-006-1497-3
– volume: 309
  start-page: 17
  issue: 1–4
  year: 2005
  ident: 10.1016/j.watres.2023.119745_bib0056
  article-title: Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2004.11.010
– volume: 159
  start-page: 65
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0035
  article-title: Predicting groundwater arsenic contamination: regions at risk in highest populated state of India
  publication-title: Water Res.
  doi: 10.1016/j.watres.2019.04.054
– ident: 10.1016/j.watres.2023.119745_bib0014
  doi: 10.21203/rs.3.rs-1028294/v1
– volume: 1
  start-page: 295
  issue: 3
  year: 1989
  ident: 10.1016/j.watres.2023.119745_bib0025
  article-title: Unsupervised learning
  publication-title: Neural Comput
  doi: 10.1162/neco.1989.1.3.295
– volume: 30
  start-page: 883
  issue: 3
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0028
  article-title: A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran
  publication-title: Stochastic Environmen. Res. Risk Assess.
  doi: 10.1007/s00477-015-1088-3
– volume: 712
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0054
  article-title: Modeling arsenic content in Brazilian soils: what is relevant?
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2020.136511
– year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0219
  article-title: Hardware for machine learning: challenges and opportunities
– volume: 28
  start-page: 3569
  issue: 5
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0081
  article-title: Application of soft computing models for simulating nitrate contamination in groundwater: comprehensive review, assessment and future opportunities
  publication-title: Arch. Comput. Meth. Eng.
  doi: 10.1007/s11831-020-09513-2
– volume: 45
  start-page: 5535
  issue: 17
  year: 2011
  ident: 10.1016/j.watres.2023.119745_bib0045
  article-title: Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network
  publication-title: Water Res.
  doi: 10.1016/j.watres.2011.08.010
– volume: 44
  start-page: 5149
  issue: 9
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0088
  article-title: Meta-learning in neural networks: a survey
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– volume: 10
  issue: 4
  year: 2014
  ident: 10.1016/j.watres.2023.119745_bib0109
  article-title: ANN-based estimation of groundwater quality using a wireless water quality network
  publication-title: Int. J. Distrib. Sens. Netw.
  doi: 10.1155/2014/458329
– volume: 552
  start-page: 44
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0059
  article-title: The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2017.06.019
– start-page: 380
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0121
  article-title: Genetic Algorithm- A Literature Review
– volume: 21
  start-page: 71
  issue: 1
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0016
  article-title: Predicting Nitrate Concentration and Its Spatial Distribution in Groundwater Resources Using Support Vector Machines (SVMs) Model
  publication-title: Environmen. Model. Assess.
  doi: 10.1007/s10666-015-9468-0
– volume: 398
  start-page: 212
  issue: 3–4
  year: 2011
  ident: 10.1016/j.watres.2023.119745_bib0024
  article-title: Artificial neural network model as a potential alternative for groundwater salinity forecasting
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2010.12.016
– volume: 13
  start-page: 912
  issue: 18
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0165
  article-title: Groundwater quality assessment using random forest method based on groundwater quality indices (case study: miandoab plain aquifer, NW of Iran)
  publication-title: Arabian J. Geosci.
  doi: 10.1007/s12517-020-05904-8
– volume: 185
  start-page: 3445
  issue: 4
  year: 2013
  ident: 10.1016/j.watres.2023.119745_bib0132
  article-title: Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks
  publication-title: Environ. Monit. Assess
  doi: 10.1007/s10661-012-2802-y
– start-page: 393
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0119
  article-title: A review of application of artificial neural network in ground water modeling
– volume: 12
  start-page: 58
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0203
– volume: 2
  start-page: 1
  issue: 1
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0201
  article-title: Artificial intelligence for the prediction of water quality index in groundwater systems
  publication-title: Modeling Earth Syst. Environ.
– start-page: 14
  issue: 4
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0225
  article-title: Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia
  publication-title: Sustainability (Switzerland)
– volume: 152
  issue: April
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0076
  article-title: A workflow to address pitfalls and challenges in applying machine learning models to hydrology
  publication-title: Adv. Water Resour.
– volume: 59
  start-page: 352
  issue: 3
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0217
  article-title: Machine learning predictions of pH in the glacial aquifer system, Northern USA
  publication-title: Groundwater
  doi: 10.1111/gwat.13063
– volume: 52
  start-page: 9889
  issue: 17
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0177
  article-title: Prediction Modeling and Mapping of Groundwater Fluoride Contamination throughout India
  publication-title: Environ. Sci. Technol.
  doi: 10.1021/acs.est.8b01679
– volume: 25
  start-page: 197
  issue: 2
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0034
  article-title: A random forest guided tour
  publication-title: test.
  doi: 10.1007/s11749-016-0481-7
– volume: 21
  start-page: 134
  issue: 1
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0169
  article-title: Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran
  publication-title: KSCE J. Civ. Eng.
  doi: 10.1007/s12205-016-0572-8
– start-page: 645
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0254
  article-title: Hardware Acceleration for Machine Learning
– volume: 186
  start-page: 35
  issue: 1
  year: 2014
  ident: 10.1016/j.watres.2023.119745_bib0007
  article-title: Modeling of nitrate concentration in groundwater using artificial intelligence approach—A case study of Gaza coastal aquifer
  publication-title: Environ. Monit. Assess
  doi: 10.1007/s10661-013-3353-6
– volume: 245
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0061
  article-title: Groundwater quality forecasting using machine learning algorithms for irrigation purposes
  publication-title: Agric. Water Manage.
  doi: 10.1016/j.agwat.2020.106625
– volume: 36
  start-page: 2138
  issue: 10
  year: 2002
  ident: 10.1016/j.watres.2023.119745_bib0162
  article-title: Probability of Nitrate Contamination of Recently Recharged Groundwaters in the Conterminous United States
  publication-title: Environ. Sci. Technol.
  doi: 10.1021/es0113854
– volume: 46
  start-page: 901
  issue: 2
  year: 2012
  ident: 10.1016/j.watres.2023.119745_bib0163
  article-title: Verifiable Metamodels for Nitrate Losses to Drains and Groundwater in the Corn Belt, USA
  publication-title: Environ. Sci. Technol.
  doi: 10.1021/es202875e
– volume: 2022
  start-page: 726
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0211
  article-title: Comparative analysis on ensemble learning techniques for groundwater quality assessment of chhattisgarh region
– volume: 585
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0224
  article-title: A survey on river water quality modelling using artificial intelligence models: 2000–2020
  publication-title: J. Hydrol. (Amst)
– volume: 66
  start-page: 1
  issue: 1
  year: 1994
  ident: 10.1016/j.watres.2023.119745_bib0038
  article-title: Fuzzy neural networks: a survey
  publication-title: Fuzzy Sets Syst.
  doi: 10.1016/0165-0114(94)90297-6
– volume: 41
  start-page: 893
  issue: 5
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0204
  article-title: Hydrogeochemical processes and multivariate analysis for groundwater quality in the arid Maadher region of Hodna, northern Algeria
  publication-title: Acta Geochimica
  doi: 10.1007/s11631-022-00553-y
– start-page: 13
  issue: 9
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0001
  article-title: Exploring artificial intelligence techniques for groundwater quality assessment
  publication-title: Water (Basel)
– volume: 0
  start-page: 1
  issue: 0
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0207
  article-title: Improving Water Quality Index prediction for water resources management plans in Malaysia: application of machine learning techniques
  publication-title: Geocarto Int.
– volume: 42
  start-page: 1
  year: 2013
  ident: 10.1016/j.watres.2023.119745_bib0008
  article-title: Wavelet neural networks: a practical guide
  publication-title: Neural Networks
  doi: 10.1016/j.neunet.2013.01.008
– volume: 54
  start-page: 995
  issue: 3–4
  year: 2011
  ident: 10.1016/j.watres.2023.119745_bib0091
  article-title: Spatial distribution pattern analysis of groundwater nitrate nitrogen pollution in Shandong intensive farming regions of China using neural network method
  publication-title: Math Comput. Model.
  doi: 10.1016/j.mcm.2010.11.027
– volume: 48
  start-page: 840
  issue: 3
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0157
  article-title: Classification of groundwater chemistry in Shimabara, using self-organizing maps
  publication-title: Hydrol. Res.
  doi: 10.2166/nh.2016.072
– volume: 38
  start-page: 367
  issue: 4
  year: 2002
  ident: 10.1016/j.watres.2023.119745_bib0073
  article-title: Stochastic gradient boosting
  publication-title: Computat. Statist. \& Data Analy.
  doi: 10.1016/S0167-9473(01)00065-2
– volume: 833
  issue: October 2021
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0176
  article-title: Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh – Machine learning spatial prediction modeling and comparison with arsenic
  publication-title: Sci. Total Environ.
– volume: 7
  start-page: 64
  issue: 1
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0143
  article-title: Application of Machine learning algorithms in investigation of groundwater quality parameters over YSR district, India
  publication-title: Turkish J. Engin.
  doi: 10.31127/tuje.1032314
– volume: 574
  start-page: 744
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0153
  article-title: Groundwater DRASTIC vulnerability mapping by unsupervised and supervised techniques using a modelling strategy in two levels
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2019.04.039
– volume: 66
  start-page: 86
  issue: 2
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0085
  article-title: A practical low-cost model for prediction of the groundwater quality using artificial neural networks
  publication-title: J. Water Supply
  doi: 10.2166/aqua.2017.035
– volume: 192
  start-page: 776
  issue: 12
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0031
  article-title: Comparative evaluation of machine learning models for groundwater quality assessment
  publication-title: Environ. Monit. Assess.
  doi: 10.1007/s10661-020-08695-3
– volume: 14
  start-page: 206
  issue: 1
  year: 2011
  ident: 10.1016/j.watres.2023.119745_bib0017
  article-title: Statistical approaches and hydrochemical modelling of groundwater system in a small tropical island
  publication-title: J. Hydroinf.
  doi: 10.2166/hydro.2011.072
– volume: 96
  start-page: 146
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0019
  article-title: Fuzzy vulnerability mapping of urban groundwater systems to nitrate contamination
  publication-title: Environmen. Modell. Software
  doi: 10.1016/j.envsoft.2017.06.043
– volume: 57
  start-page: 12227
  issue: 26
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0173
  article-title: Development of enhanced groundwater arsenic prediction model using machine learning approaches in Southeast Asian countries
  publication-title: Desalination Water Treat
  doi: 10.1080/19443994.2015.1049411
– volume: 31
  start-page: 1769
  issue: 2
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0158
  article-title: Groundwater quality evaluation based on PCA-PSO-SVM machine learning in Xinzhou City, China
  publication-title: Polish J. Environmen. Stud.
  doi: 10.15244/pjoes/140170
– volume: 50
  start-page: 637
  issue: 5
  year: 2006
  ident: 10.1016/j.watres.2023.119745_bib0236
  article-title: Prediction of agriculture derived groundwater nitrate distribution in North China Plain with GIS-based BPNN
  publication-title: Environ. Geol.
  doi: 10.1007/s00254-006-0237-x
– volume: 15
  start-page: 497
  issue: 1
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0164
  article-title: Nitrate and Sulfate Estimations in Water Sources Using a Planar Electromagnetic Sensor Array and Artificial Neural Network Method
  publication-title: IEEE Sens. J.
  doi: 10.1109/JSEN.2014.2347996
– volume: 56
  start-page: 19
  issue: 1
  year: 2008
  ident: 10.1016/j.watres.2023.119745_bib0249
  article-title: Neural network prediction of nitrate in groundwater of Harran Plain, Turkey
  publication-title: Environ. Geol.
  doi: 10.1007/s00254-007-1136-5
– volume: 54
  start-page: 1013
  issue: 2
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0255
  article-title: On lack of robustness in hydrological model development due to absence of guidelines for selecting calibration and evaluation data: demonstration for data-driven models
  publication-title: Water Resour. Res.
  doi: 10.1002/2017WR021470
– volume: 580
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0072
  article-title: Comparison of four learning-based methods for predicting groundwater redox status
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2019.124200
– volume: 2
  start-page: 1
  issue: 1
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0026
  article-title: Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction
  publication-title: Modeling Earth Syst. Environ.
– volume: 20
  start-page: 851
  issue: 7
  year: 2005
  ident: 10.1016/j.watres.2023.119745_bib0011
  article-title: Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data
  publication-title: Environmen. Modell. Software
  doi: 10.1016/j.envsoft.2004.05.001
– volume: 73
  start-page: 5333
  issue: 9
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0099
  article-title: Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey)
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-014-3784-6
– volume: 7
  start-page: 43
  issue: 2
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0032
  article-title: Water Quality Prediction Using Statistical Tool and Machine Learning Algorithm
  publication-title: Int. J. Chemoinform. Chem. Engin.
– year: 1992
  ident: 10.1016/j.watres.2023.119745_bib0004
– year: 1992
  ident: 10.1016/j.watres.2023.119745_bib0047
– volume: 3
  start-page: 36
  issue: 1
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0232
  article-title: Estimation of nitrate concentration in groundwater of Kadava river basin-Nashik district, Maharashtra, India by using artificial neural network model
  publication-title: Model. Earth Syst. Environ.
  doi: 10.1007/s40808-017-0290-3
– volume: 32
  start-page: 3023
  issue: 9
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0151
  article-title: Mapping aquifer vulnerability indices using artificial intelligence-running multiple frameworks (AIMF) with supervised and unsupervised learning
  publication-title: Water Resour. Manage.
  doi: 10.1007/s11269-018-1971-z
– year: 1999
  ident: 10.1016/j.watres.2023.119745_bib0086
– volume: 49
  start-page: 503
  issue: 3
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0096
  article-title: Use of GIS, Statistics and Machine Learning for Groundwater Quality Management: application to Nitrate Contamination
  publication-title: Water Resour.
  doi: 10.1134/S0097807822030162
– volume: 50
  start-page: 7332
  issue: 9
  year: 2014
  ident: 10.1016/j.watres.2023.119745_bib0009
  article-title: Blind source separation for groundwater pressure analysis based on nonnegative matrix factorization
  publication-title: Water Resour. Res.
  doi: 10.1002/2013WR015037
– volume: 3
  start-page: 193
  issue: 3
  year: 2001
  ident: 10.1016/j.watres.2023.119745_bib0087
  article-title: Intelligent characterisation and diagnosis of the groundwater quality in an urban fractured-rock aquifer using an artificial neural network
  publication-title: Urban Water
  doi: 10.1016/S1462-0758(01)00045-0
– start-page: 144
  year: 1992
  ident: 10.1016/j.watres.2023.119745_bib0036
  article-title: A Training Algorithm for Optimal Margin Classifiers
– volume: 59
  start-page: 201
  year: 2012
  ident: 10.1016/j.watres.2023.119745_bib0229
  article-title: Data-driven modelling of groundwater vulnerability to nitrate pollution in Slovenia (Podatkovno vodeno modeliranje ranljivosti podzemne vode na nitratno onesnaženje v Sloveniji)
  publication-title: RMZ—Mater. Geoenviron
– volume: 61
  start-page: 6
  issue: 6
  year: 2010
  ident: 10.1016/j.watres.2023.119745_bib0142
  article-title: Forecasting study for nitrate ion removal using reactive barriers
  publication-title: REV. CHIM.(Bucharest)
– volume: 24
  start-page: 8562
  issue: 9
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0152
  article-title: Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models
  publication-title: Environmen. Sci. Pollut. Res.
  doi: 10.1007/s11356-017-8489-4
– volume: 26
  start-page: 849
  issue: 3
  year: 1997
  ident: 10.1016/j.watres.2023.119745_bib0160
  article-title: Selenium in irrigated agricultural areas of the Western United States
  publication-title: J. Environ. Qual.
  doi: 10.2134/jeq1997.00472425002600030035x
– volume: 548
  start-page: 170
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0167
  article-title: Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds
  publication-title: J Hydrol (Amst)
  doi: 10.1016/j.jhydrol.2017.03.002
– volume: 531
  start-page: 902
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0161
  article-title: A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2015.10.025
– year: 2003
  ident: 10.1016/j.watres.2023.119745_bib0185
– volume: 16
  start-page: 131
  issue: 2
  year: 2003
  ident: 10.1016/j.watres.2023.119745_bib0090
  article-title: A fuzzy process controller for in situ groundwater bioremediation
  publication-title: Eng. Appl. Artif. Intell.
  doi: 10.1016/S0952-1976(03)00054-X
– volume: 644
  start-page: 954
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0200
  article-title: A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2018.07.054
– start-page: 14
  issue: 10
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0125
  article-title: Imputation of Ammonium Nitrogen Concentration in Groundwater Based on a Machine Learning Method
  publication-title: Water (Switzerland)
– volume: 3
  issue: 4
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0144
  article-title: Prediction and modeling of fluoride concentrations in groundwater resources using an artificial neural network: a case study in Khaf
  publication-title: Environmen. Heal. Engin. Manag. J.
– volume: 54
  start-page: 4785
  issue: 7
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0234
  article-title: The Impact of Landscape Characteristics on Groundwater Dissolved Organic Nitrogen: insights From Machine Learning Methods and Sensitivity Analysis
  publication-title: Water Resour Res
  doi: 10.1029/2017WR021749
– volume: 30
  start-page: 1589
  issue: 5
  year: 1994
  ident: 10.1016/j.watres.2023.119745_bib0188
  article-title: Using genetic algorithms to solve a multiple objective groundwater pollution containment problem
  publication-title: Water Resour. Res.
  doi: 10.1029/93WR03511
– volume: 12
  start-page: 251
  issue: 3
  year: 2010
  ident: 10.1016/j.watres.2023.119745_bib0135
  article-title: Prediction of weekly nitrate-N fluctuations in a small agricultural watershed in Illinois
  publication-title: J. Hydroinf.
  doi: 10.2166/hydro.2010.064
– volume: 184
  start-page: 255
  year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0230
  article-title: A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices
  publication-title: J. Environ. Manage.
  doi: 10.1016/j.jenvman.2016.09.082
– volume: 3
  start-page: 1
  issue: 1
  year: 2009
  ident: 10.1016/j.watres.2023.119745_bib0257
  article-title: Introduction to semi-supervised learning
  publication-title: Synthesis Lectures Artif. Intell. Mach. Learn.
  doi: 10.1007/978-3-031-01548-9
– start-page: 1893
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0242
  article-title: Parameter estimation of nonlinear nitrate prediction model using genetic algorithm
– start-page: 585
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0145
  article-title: Application of machine learning algorithms in hydrology
  publication-title: Comput. Earth Environmen. Sci.
  doi: 10.1016/B978-0-323-89861-4.00027-0
– volume: 31
  start-page: 2705
  issue: 10
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0027
  article-title: Comparison of machine learning models for predicting fluoride contamination in groundwater
  publication-title: Stochast. Environmen. Res. Risk Assess.
  doi: 10.1007/s00477-016-1338-z
– volume: 183
  start-page: 29
  issue: 1
  year: 2005
  ident: 10.1016/j.watres.2023.119745_bib0198
  article-title: Pesticide prediction in ground water in North Carolina domestic wells using artificial neural networks
  publication-title: Ecol. Modell.
  doi: 10.1016/j.ecolmodel.2004.07.021
– volume: 36
  start-page: 451
  issue: 2
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0078
  article-title: Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: geospatial Artificial Intelligence
  publication-title: Water Resour. Manage.
  doi: 10.1007/s11269-021-02969-2
– volume: 21
  start-page: 768
  year: 1965
  ident: 10.1016/j.watres.2023.119745_bib0071
  article-title: Cluster analysis of multivariate data: efficiency versus interpretability of classifications
  publication-title: Biometrics
– volume: 587
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0023
  article-title: Estimation of total dissolved solids (TDS) using new hybrid machine learning models
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2020.124989
– volume: 276
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0213
  article-title: Prediction of groundwater quality using efficient machine learning technique
  publication-title: Chemosphere
  doi: 10.1016/j.chemosphere.2021.130265
– ident: 10.1016/j.watres.2023.119745_bib0241
– volume: 76
  start-page: 1
  issue: 17
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0092
  article-title: Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-017-6938-5
– volume: 113
  start-page: 54
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0172
  article-title: Continual lifelong learning with neural networks: a review
  publication-title: Neural. Netw.
  doi: 10.1016/j.neunet.2019.01.012
– volume: 15
  start-page: 64004
  issue: 6
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0112
  article-title: Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning
  publication-title: Environ. Res. Lett.
  doi: 10.1088/1748-9326/ab7d5c
– volume: 55
  start-page: 5012
  issue: 8
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0130
  article-title: Machine learning models of arsenic in private wells throughout the conterminous united states as a tool for exposure assessment in human health studies
  publication-title: Environ. Sci. Technol.
  doi: 10.1021/acs.est.0c05239
– volume: 624
  start-page: 661
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0190
  article-title: Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2017.12.152
– volume: 31
  start-page: 241
  issue: 3
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0259
  article-title: Groundwater quality modeling: On the analogy between integrative PSO and MRFO mathematical and machine learning models
  publication-title: Environ. Qual. Manage.
  doi: 10.1002/tqem.21775
– volume: 32
  start-page: 421
  issue: 4
  year: 2006
  ident: 10.1016/j.watres.2023.119745_bib0227
  article-title: Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system
  publication-title: Comput. Geosci.
  doi: 10.1016/j.cageo.2005.07.003
– volume: 220
  start-page: 6
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0021
  article-title: Prediction of annual drinking water quality reduction based on Groundwater Resource Index using the artificial neural network and fuzzy clustering
  publication-title: J. Contam. Hydrol.
  doi: 10.1016/j.jconhyd.2018.10.010
– year: 2016
  ident: 10.1016/j.watres.2023.119745_bib0110
– volume: 7
  start-page: 3633
  issue: 7
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0079
  article-title: A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)
  publication-title: Appl. Water Sci.
  doi: 10.1007/s13201-016-0508-y
– volume: 6
  start-page: 3149
  issue: 8
  year: 2013
  ident: 10.1016/j.watres.2023.119745_bib0215
  article-title: Neural network assessment of groundwater contamination of US Mid-continent
  publication-title: Arabian J. Geosci.
  doi: 10.1007/s12517-012-0570-1
– volume: 807
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0186
  article-title: Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2021.151065
– volume: 601–602
  start-page: 1160
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0187
  article-title: A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2017.05.192
– volume: 54
  issue: 12
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0066
  article-title: Predicting geogenic Arsenic in Drinking Water Wells in Glacial Aquifers, North-Central USA: accounting for Depth-Dependent Features
  publication-title: Water Resour. Res.
  doi: 10.1029/2018WR023106
– volume: 6
  issue: 1
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0006
  article-title: Modeling Water Quality Parameters Using Data-Driven Models, a Case Study Abu-Ziriq Marsh in South of Iraq
  publication-title: Hydrology
  doi: 10.3390/hydrology6010024
– volume: 63
  start-page: 169
  issue: 3
  year: 2003
  ident: 10.1016/j.watres.2023.119745_bib0206
  article-title: Neural networks for predicting nitrate-nitrogen in drainage water
  publication-title: Agric. Water Manage.
  doi: 10.1016/S0378-3774(03)00159-8
– volume: 20
  start-page: 829
  issue: 5
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0117
  article-title: Assessment of groundwater arsenic contamination using machine learning in Varanasi, Uttar Pradesh, India
  publication-title: J. Water Heal.
  doi: 10.2166/wh.2022.015
– volume: 10
  start-page: 128
  issue: 6
  year: 2017
  ident: 10.1016/j.watres.2023.119745_bib0042
  article-title: Evaluation of ground water quality contaminants using linear regression and artificial neural network models
  publication-title: Arabian J. Geosci.
  doi: 10.1007/s12517-017-2867-6
– volume: 137
  start-page: 73
  year: 2014
  ident: 10.1016/j.watres.2023.119745_bib0046
  article-title: Hydrogeochemical interpretation of South Korean groundwater monitoring data using Self-Organizing Maps
  publication-title: J. Geochem. Explor.
  doi: 10.1016/j.gexplo.2013.12.001
– volume: 6
  start-page: 21
  issue: 3
  year: 2006
  ident: 10.1016/j.watres.2023.119745_bib0178
  article-title: Ensemble based systems in decision making
  publication-title: IEEE Circuits Syst. Mag.
  doi: 10.1109/MCAS.2006.1688199
– volume: 28
  start-page: 129
  issue: 2
  year: 1982
  ident: 10.1016/j.watres.2023.119745_bib0129
  article-title: Least squares quantization in PCM
  publication-title: IEEE Trans. Inf. Theory
  doi: 10.1109/TIT.1982.1056489
– volume: 28
  start-page: 10804
  issue: 9
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0147
  article-title: Susceptibility mapping of groundwater salinity using machine learning models
  publication-title: Environmen. Sci. Pollut. Res.
  doi: 10.1007/s11356-020-11319-5
– volume: 8
  start-page: 903
  issue: 2
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0104
  article-title: Evaluation of ANFIS, ANN, and geostatistical models to spatial distribution of groundwater quality (case study: mashhad plain in Iran)
  publication-title: Arabian J. Geosci.
  doi: 10.1007/s12517-013-1179-8
– volume: 147
  start-page: 938
  issue: 3
  year: 2007
  ident: 10.1016/j.watres.2023.119745_bib0050
  article-title: Analysis of groundwater quality using fuzzy synthetic evaluation
  publication-title: J. Hazard. Mater.
  doi: 10.1016/j.jhazmat.2007.01.119
– year: 2012
  ident: 10.1016/j.watres.2023.119745_bib0015
– year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0048
  article-title: Fuzzy Logic
– year: 2002
  ident: 10.1016/j.watres.2023.119745_bib0055
– year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0003
  article-title: Integrating machine learning and multi-linear regression modeling approaches in groundwater quality assessment around Obosi, SE Nigeria
  publication-title: Environ., Develop. Sustainab.
– volume: 7
  start-page: 73
  issue: 2
  year: 2012
  ident: 10.1016/j.watres.2023.119745_bib0148
  article-title: Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system
  publication-title: Soil Water Res.
  doi: 10.17221/46/2010-SWR
– volume: 101
  start-page: 403
  issue: 3
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0095
  article-title: Spatial prediction of nitrate concentration using GIS and ANFIS modelling in groundwater
  publication-title: Bull. Environ. Contam. Toxicol.
  doi: 10.1007/s00128-018-2406-5
– start-page: 13
  issue: 4
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0243
  article-title: Distribution of Groundwater Arsenic in Uruguay Using Hybrid Machine Learning and Expert System Approaches
  publication-title: Water (Basel)
– volume: 380
  year: 2020
  ident: 10.1016/j.watres.2023.119745_bib0128
  article-title: Machine learning and transport simulations for groundwater anomaly detection
  publication-title: J. Comput. Appl. Math
  doi: 10.1016/j.cam.2020.112982
– volume: 621
  start-page: 697
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0029
  article-title: Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2017.11.185
– year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0202
– volume: 81
  start-page: 1
  issue: 2
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0214
  article-title: Knowledge-driven and machine learning decision tree-based approach for assessment of geospatial variation of groundwater quality around coal mining regions, Korba district, Central India
  publication-title: Environ. Earth Sci
  doi: 10.1007/s12665-021-10147-1
– volume: 20
  start-page: 542
  issue: 3
  year: 2009
  ident: 10.1016/j.watres.2023.119745_bib0041
  article-title: Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]
  publication-title: IEEE Trans. Neural Networks
  doi: 10.1109/TNN.2009.2015974
– year: 2012
  ident: 10.1016/j.watres.2023.119745_bib0141
– volume: 536
  start-page: 481
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0239
  article-title: Modeling groundwater nitrate concentrations in private wells in Iowa
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2015.07.080
– volume: 7
  start-page: 53040
  year: 2019
  ident: 10.1016/j.watres.2023.119745_bib0210
  article-title: Review of deep learning algorithms and architectures
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2912200
– volume: 20
  start-page: 65
  issue: 1
  year: 2015
  ident: 10.1016/j.watres.2023.119745_bib0105
  article-title: Groundwater quality assessment using artificial neural network: a case study of Bahabad plain, Yazd, Iran
  publication-title: Desert
– volume: 5
  start-page: 143
  issue: 3
  year: 2004
  ident: 10.1016/j.watres.2023.119745_bib0212
  article-title: Groundwater pollution source identification and simultaneous parameter estimation using pattern matching by artificial neural network
  publication-title: Environ. Forensics
  doi: 10.1080/15275920490495873
– start-page: 14
  issue: 15
  year: 2022
  ident: 10.1016/j.watres.2023.119745_bib0013
  article-title: An Integrated Bayesian and Machine Learning Approach Application to Identification of Groundwater Contamination Source Parameters
  publication-title: Water (Switzerland)
– volume: 57
  issue: 4
  year: 2021
  ident: 10.1016/j.watres.2023.119745_bib0067
  article-title: Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States
  publication-title: Water Resour. Res.
  doi: 10.1029/2020WR028207
– volume: 227
  start-page: 415
  year: 2018
  ident: 10.1016/j.watres.2023.119745_bib0154
  article-title: Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks
  publication-title: J. Environ. Manage.
  doi: 10.1016/j.jenvman.2018.08.019
– volume: 376
  start-page: 275
  issue: 1–2
  year: 2009
  ident: 10.1016/j.watres.2023.119745_bib0137
  article-title: Discriminating sources of nitrate pollution in an unconfined sandy aquifer
  publication-title: J. Hydrol. (Amst)
  doi: 10.1016/j.jhydrol.2009.07.039
– volume: 193
  start-page: 629
  issue: 3–4
  year: 2006
  ident: 10.1016/j.watres.2023.119745_bib0175
  article-title: Metamodelling: theory, concepts and application to nitrate leaching modelling
  publication-title: Ecol. Modell.
  doi: 10.1016/j.ecolmodel.2005.08.045
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Snippet •Reviewed more than 200 papers that used machine learning in groundwater quality modeling.•Neural networks are the most used machine learning model in...
Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical...
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SubjectTerms Artificial Intelligence
Deep learning
Environmental Monitoring - methods
Groundwater
groundwater contamination
Groundwater quality
Iran
Machine Learning
Neural Networks, Computer
nitrates
prediction
water quality
Title Application of machine learning in groundwater quality modeling - A comprehensive review
URI https://dx.doi.org/10.1016/j.watres.2023.119745
https://www.ncbi.nlm.nih.gov/pubmed/36812816
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