Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction

Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water r...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Water resources research Ročník 56; číslo 4
Hlavní autori:	Chadalawada, Jayashree, Herath, H. M. V. V., Babovic, Vladan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Washington John Wiley & Sons, Inc 01.04.2020
Predmet:	Alabama Algorithms Atmospheric conditions Atmospheric models Blackwater rivers Catchment scale Catchments Computation Configurations Evolutionary algorithms Fieldwork flexible conceptual modeling framework Genetic algorithms genetic programming Hydrologic models Hydrology Learning algorithms Machine learning Modelling Neural networks Rain Rainfall Rainfall-runoff relationships rainfall‐runoff modeling River basins Rivers Runoff time series analysis Water resources Water resources research Watersheds Alabama
ISSN:	0043-1397, 1944-7973
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water resources research community is dedicated to development of new models that perform well under specific atmospheric conditions and catchment properties. In this context, flexible modeling frameworks are gaining importance as they facilitate the model building process by providing the model building blocks, whereby the hydrologist is free to assemble the model for task at hand. Such flexible models have high degree of transferability, which in turn aid in progressing toward a unified hydrological theory at catchment scale. However, in cases without sufficient insights regarding a catchment characteristics and/or lack of expert's knowledge, one may have to try a large number of model configurations based on available model building blocks to construct an appropriate model for the catchment of interest. Undoubtedly, this may be time consuming and computationally intensive. This paper proposes a novel model building algorithm, which uses the full potential of flexible modeling frameworks by searching the model space and inferring suitable model configurations relying on machine learning. Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming (GP). State‐of‐art GP applications in rainfall‐runoff modeling so far used the algorithm as a short‐term forecasting tool that generates an expected future time series very similar to neural networks application. In this case, the proposed algorithm develops a physically meaningful rainfall‐runoff model. Although at the moment we learn models using two flexible modeling frameworks (SUPERFLEX and FUSE), the model induction toolkit can be armed with any internal coherence building blocks. The model induction capabilities of the proposed framework have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model induction toolkit are consistent with the fieldwork investigations and previously reported research findings. Key Points This paper presents a novel machine learning algorithm, which is guided through the incorporation of existing hydrological knowledge Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming In the present case, the building blocks of flexible hydrological modeling frameworks represent elements of the background knowledge
AbstractList	Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water resources research community is dedicated to development of new models that perform well under specific atmospheric conditions and catchment properties. In this context, flexible modeling frameworks are gaining importance as they facilitate the model building process by providing the model building blocks, whereby the hydrologist is free to assemble the model for task at hand. Such flexible models have high degree of transferability, which in turn aid in progressing toward a unified hydrological theory at catchment scale. However, in cases without sufficient insights regarding a catchment characteristics and/or lack of expert's knowledge, one may have to try a large number of model configurations based on available model building blocks to construct an appropriate model for the catchment of interest. Undoubtedly, this may be time consuming and computationally intensive. This paper proposes a novel model building algorithm, which uses the full potential of flexible modeling frameworks by searching the model space and inferring suitable model configurations relying on machine learning. Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming (GP). State‐of‐art GP applications in rainfall‐runoff modeling so far used the algorithm as a short‐term forecasting tool that generates an expected future time series very similar to neural networks application. In this case, the proposed algorithm develops a physically meaningful rainfall‐runoff model. Although at the moment we learn models using two flexible modeling frameworks (SUPERFLEX and FUSE), the model induction toolkit can be armed with any internal coherence building blocks. The model induction capabilities of the proposed framework have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model induction toolkit are consistent with the fieldwork investigations and previously reported research findings. This paper presents a novel machine learning algorithm, which is guided through the incorporation of existing hydrological knowledge Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming In the present case, the building blocks of flexible hydrological modeling frameworks represent elements of the background knowledge Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water resources research community is dedicated to development of new models that perform well under specific atmospheric conditions and catchment properties. In this context, flexible modeling frameworks are gaining importance as they facilitate the model building process by providing the model building blocks, whereby the hydrologist is free to assemble the model for task at hand. Such flexible models have high degree of transferability, which in turn aid in progressing toward a unified hydrological theory at catchment scale. However, in cases without sufficient insights regarding a catchment characteristics and/or lack of expert's knowledge, one may have to try a large number of model configurations based on available model building blocks to construct an appropriate model for the catchment of interest. Undoubtedly, this may be time consuming and computationally intensive. This paper proposes a novel model building algorithm, which uses the full potential of flexible modeling frameworks by searching the model space and inferring suitable model configurations relying on machine learning. Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming (GP). State‐of‐art GP applications in rainfall‐runoff modeling so far used the algorithm as a short‐term forecasting tool that generates an expected future time series very similar to neural networks application. In this case, the proposed algorithm develops a physically meaningful rainfall‐runoff model. Although at the moment we learn models using two flexible modeling frameworks (SUPERFLEX and FUSE), the model induction toolkit can be armed with any internal coherence building blocks. The model induction capabilities of the proposed framework have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model induction toolkit are consistent with the fieldwork investigations and previously reported research findings. Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water resources research community is dedicated to development of new models that perform well under specific atmospheric conditions and catchment properties. In this context, flexible modeling frameworks are gaining importance as they facilitate the model building process by providing the model building blocks, whereby the hydrologist is free to assemble the model for task at hand. Such flexible models have high degree of transferability, which in turn aid in progressing toward a unified hydrological theory at catchment scale. However, in cases without sufficient insights regarding a catchment characteristics and/or lack of expert's knowledge, one may have to try a large number of model configurations based on available model building blocks to construct an appropriate model for the catchment of interest. Undoubtedly, this may be time consuming and computationally intensive. This paper proposes a novel model building algorithm, which uses the full potential of flexible modeling frameworks by searching the model space and inferring suitable model configurations relying on machine learning. Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming (GP). State‐of‐art GP applications in rainfall‐runoff modeling so far used the algorithm as a short‐term forecasting tool that generates an expected future time series very similar to neural networks application. In this case, the proposed algorithm develops a physically meaningful rainfall‐runoff model. Although at the moment we learn models using two flexible modeling frameworks (SUPERFLEX and FUSE), the model induction toolkit can be armed with any internal coherence building blocks. The model induction capabilities of the proposed framework have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model induction toolkit are consistent with the fieldwork investigations and previously reported research findings. Key Points This paper presents a novel machine learning algorithm, which is guided through the incorporation of existing hydrological knowledge Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming In the present case, the building blocks of flexible hydrological modeling frameworks represent elements of the background knowledge
Author	Babovic, Vladan Herath, H. M. V. V. Chadalawada, Jayashree
Author_xml	– sequence: 1 givenname: Jayashree orcidid: 0000-0003-3224-1186 surname: Chadalawada fullname: Chadalawada, Jayashree organization: National University of Singapore – sequence: 2 givenname: H. M. V. V. orcidid: 0000-0002-5350-2317 surname: Herath fullname: Herath, H. M. V. V. organization: National University of Singapore – sequence: 3 givenname: Vladan orcidid: 0000-0003-4046-6473 surname: Babovic fullname: Babovic, Vladan email: vladan@nus.edu.sg organization: National University of Singapore
BookMark	eNp9kU1OHDEQhS1EJAbCLgewxIZFOvi33WY3GRFAGhTUImLZMrZ7MLhtYncrml0OkEXOmJPEw7BASMmqpFffe1Wq2ge7IQYLwAeMPmFE5AlBWN62iNSS0h0ww5KxSkhBd8EMIUYrTKXYA_s5PyCEGa_FDPy6WJsUfVw5rbxfw8vQxzRYA6-UvnfBwqVVKbiwgkWHrXKhL9yfn7_bKcS-h1fRWF_ap3AOz22wo9PwOsVVUsNQ5AJ-VrnE3cToH934nDKfxjioDfnsLjPNpEcXw3vwrqRne_hSD8C3L2c3i4tq-fX8cjFfVooRyatGaoPr2mhuMSGYaMS1wo0y_I7eKcs1sbJGFFPOlOyFJYZro6VupGhIUzf0ABxvc59S_D7ZPHaDy9p6r4KNU-5ILTgXgnFU0KM36EOcUijbdYRK1khCkSjUxy2lU8w52b57Sm5Qad1h1G1e071-TcHJG1y7UW0uMCbl_L9MdGv64bxd_3dAd9suWsI45fQvaeak2A
CitedBy_id	crossref_primary_10_1016_j_enganabound_2025_106277 crossref_primary_10_1016_j_jhydrol_2025_132705 crossref_primary_10_1007_s11356_024_34245_2 crossref_primary_10_1016_j_jhydrol_2022_128323 crossref_primary_10_1007_s12040_024_02320_4 crossref_primary_10_3390_w16213030 crossref_primary_10_1029_2023WR035876 crossref_primary_10_1002_joc_7923 crossref_primary_10_1016_j_advwatres_2021_103920 crossref_primary_10_5194_hess_25_5517_2021 crossref_primary_10_1016_j_advwatres_2024_104694 crossref_primary_10_3846_jeelm_2022_16394 crossref_primary_10_1109_JSTARS_2024_3449441 crossref_primary_10_3390_w15142572 crossref_primary_10_1016_j_jhydrol_2021_127316 crossref_primary_10_1038_s41598_024_74269_9 crossref_primary_10_3390_hydrology8010006 crossref_primary_10_1016_j_jhydrol_2020_125659 crossref_primary_10_1016_j_jhydrol_2023_130038 crossref_primary_10_3390_hydrology8020067 crossref_primary_10_1007_s00477_023_02534_w crossref_primary_10_1016_j_jhydrol_2022_128495 crossref_primary_10_1016_j_jhydrol_2023_130510 crossref_primary_10_1007_s10668_025_06284_3 crossref_primary_10_1007_s11269_024_03975_w crossref_primary_10_1038_s41598_024_77678_y crossref_primary_10_1109_JSTARS_2025_3528475 crossref_primary_10_2166_wst_2023_396 crossref_primary_10_3390_w13202871 crossref_primary_10_3390_rs15174153 crossref_primary_10_1016_j_jhydrol_2024_131504 crossref_primary_10_1016_j_ejrh_2025_102331 crossref_primary_10_1016_j_ejrh_2025_102450 crossref_primary_10_3390_w15193369 crossref_primary_10_2166_hydro_2021_111 crossref_primary_10_1038_s41598_024_75118_5 crossref_primary_10_1016_j_jhydrol_2022_128618 crossref_primary_10_1111_jfr3_70029 crossref_primary_10_2166_hydro_2021_115 crossref_primary_10_1016_j_envsoft_2024_106290 crossref_primary_10_3390_infrastructures7100137 crossref_primary_10_3390_w16020335 crossref_primary_10_1016_j_jhydrol_2023_130421 crossref_primary_10_1007_s11269_025_04093_x crossref_primary_10_1016_j_jhydrol_2021_127255 crossref_primary_10_1016_j_jhydrol_2024_130907 crossref_primary_10_1111_jfr3_70022 crossref_primary_10_1016_j_jhydrol_2025_132766 crossref_primary_10_1016_j_jhydrol_2023_129990 crossref_primary_10_1016_j_jhydrol_2024_130862 crossref_primary_10_3389_fsufs_2024_1363744 crossref_primary_10_5194_hess_26_5449_2022 crossref_primary_10_1016_j_envsoft_2025_106699 crossref_primary_10_1016_j_jhydrol_2024_131996 crossref_primary_10_1029_2020WR028148 crossref_primary_10_1016_j_jhydrol_2025_133219 crossref_primary_10_1029_2023WR035139 crossref_primary_10_1080_02626667_2025_2456211 crossref_primary_10_1016_j_advwatres_2024_104796 crossref_primary_10_2166_hydro_2023_050 crossref_primary_10_1111_1752_1688_12964 crossref_primary_10_1371_journal_pone_0255684 crossref_primary_10_5194_hess_25_4373_2021 crossref_primary_10_1109_TNNLS_2025_3545967 crossref_primary_10_1016_j_jhydrol_2022_127781 crossref_primary_10_2166_hydro_2024_192 crossref_primary_10_1016_j_jhydrol_2023_129465 crossref_primary_10_1111_jfr3_70013 crossref_primary_10_1016_j_jhydrol_2023_129269 crossref_primary_10_2166_hydro_2024_196 crossref_primary_10_2166_hydro_2024_354 crossref_primary_10_1016_j_jhydrol_2025_133329 crossref_primary_10_3390_su16041376 crossref_primary_10_1007_s40899_021_00584_y crossref_primary_10_3390_w14132053 crossref_primary_10_2166_hydro_2023_160 crossref_primary_10_1016_j_jhydrol_2024_131041 crossref_primary_10_2166_hydro_2021_011 crossref_primary_10_2166_hydro_2022_009 crossref_primary_10_1016_j_jenvman_2022_114700 crossref_primary_10_1016_j_rineng_2025_106345 crossref_primary_10_1109_ACCESS_2021_3107502 crossref_primary_10_1007_s11004_024_10167_0 crossref_primary_10_2166_hydro_2021_179 crossref_primary_10_2166_hydro_2023_246 crossref_primary_10_3390_w14050697 crossref_primary_10_2166_hydro_2023_127
Cites_doi	10.1016/j.jhydrol.2010.11.009 10.1109/ACCESS.2019.2936989 10.2166/hydro.2009.032 10.1002/hyp.3360060305 10.1007/s11269-009-9414-5 10.1061/(ASCE)1084-0699(2000)5:2(124) 10.1016/S0022-1694(03)00225-7 10.5194/tc-12-891-2018 10.5194/hess-10-139-2006 10.5194/adgeo-5-89-2005 10.1175/BAMS-D-18-0195.1 10.1002/joc.811 10.5194/hess-20-2611-2016 10.5194/hessd-10-5457-2013 10.2166/nh.2015.221 10.1007/s00477-018-1573-6 10.1002/hyp.7072 10.2166/hydro.2006.020b 10.1002/2015WR017198 10.1002/hyp.9726 10.2166/hydro.2017.078 10.1016/j.jhydrol.2016.06.026 10.1080/09715010.2015.1103201 10.1111/jawr.12259 10.1016/j.jhydrol.2015.04.032 10.1007/978-1-4757-2440-0 10.5194/hess-14-1931-2010 10.1061/(ASCE)WR.1943-5452.0000570 10.5194/hess-11-1563-2007 10.1002/2015WR017398 10.1007/978-3-319-09054-2_58 10.1109/TKDE.2017.2720168 10.1109/TKDE.2018.2861006 10.1061/40569(2001)64 10.1080/02626669609491511 10.1016/S0022-1694(01)00513-3 10.1073/pnas.1517384113 10.1016/j.jhydrol.2014.08.033 10.3808/jei.201400285 10.1126/sciadv.1602614 10.1080/02626669909492221 10.1016/j.jhydrol.2015.09.028 10.2166/hydro.2004.0016 10.5194/hess-19-209-2015 10.1016/j.jhydrol.2013.11.007 10.2166/hydro.2000.0004 10.1016/j.jher.2013.03.005 10.1029/2010WR010174 10.1007/s13369-016-2095-5 10.1029/2011WR010748 10.1016/j.jhydrol.2020.124700 10.2514/1.J055595 10.1016/j.geoderma.2017.03.013 10.1016/j.jhydrol.2009.08.003 10.2166/nh.2002.0012 10.1007/s11269-006-4007-z 10.1109/MC.2014.335 10.1017/CBO9780511627217 10.1029/2011WR010643 10.1080/02626667909491876 10.1029/2007WR006735 10.2166/hydro.2009.041 10.1023/A:1014596120381 10.1002/2014WR016731 10.1016/j.ejrh.2019.100642 10.1007/s10236-012-0524-x 10.2166/nh.1973.0013 10.1002/hyp.7902 10.1002/hyp.7936 10.1007/978-3-319-07494-8_13 10.1139/p07-037 10.1016/j.jhydrol.2018.09.043 10.1007/s00607-013-0298-0 10.1002/0470848944.hsa016 10.1016/0022-1694(70)90255-6 10.1002/9781119951001.ch1 10.1109/4235.996017 10.1111/j.1752-1688.2001.tb00980.x 10.5194/hess-5-13-2001
ContentType	Journal Article
Copyright	2020. American Geophysical Union. All Rights Reserved.
Copyright_xml	– notice: 2020. American Geophysical Union. All Rights Reserved.
DBID	AAYXX CITATION 7QH 7QL 7T7 7TG 7U9 7UA 8FD C1K F1W FR3 H94 H96 KL. KR7 L.G M7N P64 7S9 L.6
DOI	10.1029/2019WR026933
DatabaseName	CrossRef Aqualine Bacteriology Abstracts (Microbiology B) Industrial and Applied Microbiology Abstracts (Microbiology A) Meteorological & Geoastrophysical Abstracts Virology and AIDS Abstracts Water Resources Abstracts Technology Research Database Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database AIDS and Cancer Research Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Meteorological & Geoastrophysical Abstracts - Academic Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Algology Mycology and Protozoology Abstracts (Microbiology C) Biotechnology and BioEngineering Abstracts AGRICOLA AGRICOLA - Academic
DatabaseTitle	CrossRef Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Virology and AIDS Abstracts Technology Research Database Aqualine Water Resources Abstracts Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management Meteorological & Geoastrophysical Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) ASFA: Aquatic Sciences and Fisheries Abstracts AIDS and Cancer Research Abstracts Engineering Research Database Industrial and Applied Microbiology Abstracts (Microbiology A) Meteorological & Geoastrophysical Abstracts - Academic AGRICOLA AGRICOLA - Academic
DatabaseTitleList	CrossRef AGRICOLA Civil Engineering Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography Economics
EISSN	1944-7973
EndPage	n/a
ExternalDocumentID	10_1029_2019WR026933 WRCR24535
Genre	article
GeographicLocations	Alabama
GeographicLocations_xml	– name: Alabama
GroupedDBID	-~X ..I .DC 05W 0R~ 123 1OB 1OC 24P 31~ 33P 3V. 50Y 5VS 6TJ 7WY 7XC 8-1 8CJ 8FE 8FG 8FH 8FL 8G5 8R4 8R5 8WZ A00 A6W AAESR AAHBH AAHHS AAIHA AAIKC AAMNW AANHP AANLZ AASGY AAXRX AAYCA AAYJJ AAYOK AAZKR ABCUV ABJCF ABJNI ABPPZ ABTAH ABUWG ACAHQ ACBWZ ACCFJ ACCMX ACCZN ACGFO ACGFS ACIWK ACKIV ACNCT ACPOU ACPRK ACRPL ACXBN ACXQS ACYXJ ADBBV ADEOM ADKYN ADMGS ADNMO ADOZA ADXAS ADZMN AEEZP AEIGN AENEX AEQDE AEUYN AEUYR AFBPY AFGKR AFKRA AFPWT AFRAH AFWVQ AFZJQ AIDBO AIURR AIWBW AJBDE ALMA_UNASSIGNED_HOLDINGS ALUQN ALXUD AMYDB ASPBG ATCPS AVWKF AZFZN AZQEC AZVAB BDRZF BENPR BEZIV BFHJK BGLVJ BHPHI BKSAR BMXJE BPHCQ BRXPI CCPQU CS3 D0L D1J DCZOG DDYGU DPXWK DRFUL DRSTM DU5 DWQXO EBS EJD F5P FEDTE FRNLG G-S GNUQQ GODZA GROUPED_ABI_INFORM_COMPLETE GUQSH HCIFZ HVGLF HZ~ K60 K6~ L6V LATKE LEEKS LITHE LK5 LOXES LUTES LYRES M0C M2O M7R M7S MEWTI MSFUL MSSTM MVM MW2 MXFUL MXSTM MY~ O9- OHT OK1 P-X P2P P2W PALCI PATMY PCBAR PQBIZ PQBZA PQQKQ PROAC PTHSS PYCSY Q2X R.K RIWAO RJQFR ROL SAMSI SUPJJ TAE TN5 TWZ UQL VJK VOH WBKPD WXSBR WYJ XOL XSW YHZ YV5 ZCG ZY4 ZZTAW ~02 ~KM ~OA ~~A AAMMB AAYXX ADXHL AEFGJ AETEA AFFHD AGQPQ AGXDD AIDQK AIDYY AIQQE CITATION GROUPED_DOAJ PHGZM PHGZT PQGLB WIN 7QH 7QL 7T7 7TG 7U9 7UA 8FD C1K F1W FR3 H94 H96 KL. KR7 L.G M7N P64 7S9 L.6
ID	FETCH-LOGICAL-a4295-89cd166dc5e12212c05ca18ad5b3bae5c2e96031354a9f7e2d5cdc9c897828683
IEDL.DBID	WIN
ISICitedReferencesCount	101
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000538987800013&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0043-1397
IngestDate	Fri Sep 05 17:13:53 EDT 2025 Thu Sep 11 05:40:54 EDT 2025 Tue Nov 18 22:41:04 EST 2025 Sat Nov 29 01:36:44 EST 2025 Wed Jan 22 16:35:45 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	4
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-a4295-89cd166dc5e12212c05ca18ad5b3bae5c2e96031354a9f7e2d5cdc9c897828683
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0003-4046-6473 0000-0002-5350-2317 0000-0003-3224-1186
PQID	2394892307
PQPubID	105507
PageCount	23
ParticipantIDs	proquest_miscellaneous_2675577450 proquest_journals_2394892307 crossref_primary_10_1029_2019WR026933 crossref_citationtrail_10_1029_2019WR026933 wiley_primary_10_1029_2019WR026933_WRCR24535
PublicationCentury	2000
PublicationDate	April 2020 2020-04-00 20200401
PublicationDateYYYYMMDD	2020-04-01
PublicationDate_xml	– month: 04 year: 2020 text: April 2020
PublicationDecade	2020
PublicationPlace	Washington
PublicationPlace_xml	– name: Washington
PublicationTitle	Water resources research
PublicationYear	2020
Publisher	John Wiley & Sons, Inc
Publisher_xml	– name: John Wiley & Sons, Inc
References	2010; 14 2017; 3 2000; 5 2015; 142 2018; 566 1999; 44 2016; 540 2016; 188 2004; 6 2000; 2 2014; 28 2013; 7 2003; 279 2011; 397 1992; 6 2009; 11 1992; 92 1979; 24 2006; 20 2010; 25 2001 2013; 95 1999; 1173A 2019; 26 2016; 113 1986 2016; 41 2008; 22 2011; 25 2014; 51 2014; 8 2018; 32 2018; 31 1992; 1 2016; 47 1989 1992; 4 2012; 62 2017b; 21 2009; 23 2019; 7 2014; 519 2015; 19 2012 2015; 51 2006; 10 2002; 6 2002; 33 2002; 255 2009 2006; 8 2014; 47 2016; 52 2015; 529 2002; 3 1970; 10 2006 1995 2009; 377 2005 1994 2017; 29 2015; 525 1995; 2 2017; 299 2007; 11 2017a; 19 1999 2015; 25 2014; 508 2001; 5 2017; 55 2002; 22 2005; 5 2019 2016; 20 1996; 41 2015; NCAR Tech. Note NCAR/TN‐5141STR 2017 2001; 37 2008; 44 2015 2014 2011; 47 2007; 85 2013 2018; 12 2016; 9 2016; 22 1973; 4 e_1_2_10_21_1 e_1_2_10_44_1 e_1_2_10_40_1 e_1_2_10_93_1 e_1_2_10_2_1 e_1_2_10_18_1 e_1_2_10_74_1 e_1_2_10_97_1 e_1_2_10_6_1 e_1_2_10_55_1 e_1_2_10_37_1 e_1_2_10_13_1 e_1_2_10_32_1 e_1_2_10_51_1 e_1_2_10_82_1 e_1_2_10_29_1 e_1_2_10_86_1 e_1_2_10_25_1 e_1_2_10_48_1 e_1_2_10_67_1 e_1_2_10_101_1 e_1_2_10_45_1 e_1_2_10_22_1 e_1_2_10_41_1 Babovic V. (e_1_2_10_4_1) 1999 e_1_2_10_90_1 e_1_2_10_71_1 e_1_2_10_52_1 e_1_2_10_3_1 e_1_2_10_19_1 e_1_2_10_75_1 e_1_2_10_38_1 e_1_2_10_98_1 e_1_2_10_56_1 e_1_2_10_79_1 e_1_2_10_15_1 Quinlan J. R. (e_1_2_10_78_1) 1992 e_1_2_10_10_1 e_1_2_10_33_1 e_1_2_10_83_1 e_1_2_10_102_1 e_1_2_10_49_1 e_1_2_10_87_1 e_1_2_10_26_1 e_1_2_10_68_1 e_1_2_10_23_1 e_1_2_10_46_1 e_1_2_10_69_1 McClelland J. L. (e_1_2_10_64_1) 1986 Beven K. (e_1_2_10_14_1) 1995 e_1_2_10_42_1 Mast M. A. (e_1_2_10_63_1) 1999; 1173 Naghibi S. A. (e_1_2_10_70_1) 2016; 188 Koza J. R. (e_1_2_10_60_1) 1992 Oyebode O. K. (e_1_2_10_76_1) 2014; 8 Goldberg D. E. (e_1_2_10_47_1) 1989 e_1_2_10_91_1 Boukezzi Z. L. (e_1_2_10_16_1) 2016; 9 e_1_2_10_72_1 e_1_2_10_95_1 e_1_2_10_53_1 e_1_2_10_39_1 e_1_2_10_99_1 e_1_2_10_8_1 e_1_2_10_57_1 e_1_2_10_58_1 e_1_2_10_34_1 e_1_2_10_30_1 e_1_2_10_80_1 e_1_2_10_61_1 e_1_2_10_84_1 e_1_2_10_27_1 e_1_2_10_65_1 e_1_2_10_88_1 e_1_2_10_103_1 Babovic V. (e_1_2_10_7_1) 2005 Vapnik V. N. (e_1_2_10_94_1) 1992; 4 e_1_2_10_24_1 e_1_2_10_43_1 e_1_2_10_20_1 e_1_2_10_92_1 e_1_2_10_73_1 e_1_2_10_96_1 e_1_2_10_54_1 e_1_2_10_5_1 e_1_2_10_17_1 e_1_2_10_77_1 e_1_2_10_36_1 e_1_2_10_12_1 e_1_2_10_35_1 e_1_2_10_9_1 e_1_2_10_59_1 e_1_2_10_31_1 e_1_2_10_50_1 e_1_2_10_81_1 e_1_2_10_62_1 Bergström S. (e_1_2_10_11_1) 1995 e_1_2_10_85_1 e_1_2_10_28_1 e_1_2_10_66_1 e_1_2_10_100_1 e_1_2_10_89_1
References_xml	– volume: 9 start-page: 1 issue: 20 year: 2016 end-page: 12 article-title: Uncertainty analysis of HEC‐HMS model using the glue method for flash flood forecasting of Mekerra watershed, Algeria publication-title: Arabian Journal of Geosciences – volume: 92 start-page: 343 year: 1992 end-page: 348 – start-page: 331 year: 2005 end-page: 348 – year: 1989 – volume: 14 start-page: 1931 issue: 10 year: 2010 end-page: 1941 article-title: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology—Part 1: Concepts and methodology publication-title: Hydrology and Earth System Sciences – start-page: 1999 year: 2005 end-page: 2006 – volume: 47 year: 2011 article-title: Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation publication-title: Water Resources Research – volume: 20 start-page: 77 year: 2006 end-page: 90 article-title: Groundwater level forecasting in a shallow aquifer using artificial neural network approach publication-title: Water Resources Management – volume: 8 start-page: 207 issue: 3 year: 2006 end-page: 222 article-title: A symbolic data‐driven technique based on evolutionary polynomial regression publication-title: Journal of Hydroinformatics – year: 1986 – volume: 188 start-page: 1 issue: 44 year: 2016 end-page: 27 article-title: GIS‐based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran publication-title: Environmental Monitoring and Assessment – volume: 22 start-page: 2723 issue: 14 year: 2008 article-title: Do Nash values have value? Discussion and alternate proposals publication-title: Hydrological Processes – volume: 31 start-page: 1544 issue: 8 year: 2018 end-page: 1554 article-title: Machine learning for the geosciences: Challenges and opportunities publication-title: IEEE Transactions on Knowledge and Data Engineering – volume: 21 start-page: 13 year: 2017b end-page: 31 article-title: Review and comparison of performance indices for automatic model induction publication-title: Journal of Hydroinformatics – volume: 377 start-page: 80 issue: 1 year: 2009 end-page: 91 article-title: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling publication-title: Journal of Hydrology – volume: 1 year: 1992 – volume: 24 start-page: 375 issue: 3 year: 1979 end-page: 388 article-title: Automatic calibration of the tank model/l'étalonnage automatique d'un modèle à cisterne publication-title: Hydrological Sciences Journal – year: 2019 – volume: 23 start-page: 2877 year: 2009 end-page: 2894 article-title: A multivariate ANN‐wavelet approach for rainfall‐runoff modeling publication-title: Water Resources Management – volume: 44 start-page: 263 issue: 2 year: 1999 end-page: 277 article-title: GR3J: A daily watershed model with three free parameters publication-title: Hydrological Sciences Journal – volume: 37 start-page: 439 issue: 2 year: 2001 end-page: 451 article-title: Genetic programming and its application in real‐time runoff forecasting publication-title: Journal of the American Water Resources Association – volume: 5 start-page: 89 year: 2005 end-page: 97 article-title: Comparison of different efficiency criteria for hydrological model assessment publication-title: Advances in Geosciences – volume: 25 start-page: 1784 issue: 11 year: 2011 end-page: 1799 article-title: Models as multiple working hypotheses: Hydrological simulation of tropical alpine wetlands publication-title: Hydrological Processes – volume: 7 start-page: 253 issue: 4 year: 2013 end-page: 260 article-title: Prediction and simulation of monthly groundwater levels by genetic programming publication-title: Journal of Hydro‐environment Research – volume: 10 start-page: 139 year: 2006 end-page: 150 article-title: Is the groundwater reservoir linear? Learning from data in hydrological modelling publication-title: Hydrological and Earth System Sciences – volume: 25 start-page: 23 issue: 1 year: 2015 end-page: 35 article-title: The use of statistical weather generator, hybrid data driven and system dynamics models for water resources management under climate change publication-title: Journal of Environmental Informatics – volume: 2 start-page: 35 issue: 1 year: 2000 end-page: 60 article-title: Genetic programming as a model induction engine publication-title: Journal of Hydroinformatics – volume: 7 start-page: 11,9943 year: 2019 end-page: 11,9964 article-title: A machine learning metasystem for robust probabilistic nonlinear regression‐based forecasting of seasonal water availability in the us west publication-title: IEEE Access – volume: 33 start-page: 331 issue: 5 year: 2002 end-page: 346 article-title: Rainfall runoff modelling based on genetic programming publication-title: Hydrology Research – volume: 299 start-page: 1 year: 2017 end-page: 12 article-title: Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates publication-title: Geoderma – volume: 22 start-page: 148 issue: 2 year: 2016 end-page: 162 article-title: Short‐term and long‐term streamflow prediction by using ‘wavelet‐gene expression’ programming approach publication-title: ISH Journal of Hydraulic Engineering – volume: 508 start-page: 227 year: 2014 end-page: 239 article-title: Comparing and combining physically‐based and empirically‐based approaches for estimating the hydrology of ungauged catchments publication-title: Journal of Hydrology – volume: 6 start-page: 209 issue: 3 year: 2004 end-page: 223 article-title: EC‐SVM approach for real‐time hydrologic forecasting publication-title: Journal of Hydroinformatics – volume: 47 start-page: 1 issue: 1 year: 2016 end-page: 14 article-title: Comparing classical performance measures with signature indices derived from flow duration curves to assess model structures as tools for catchment classification publication-title: Hydrology Research – volume: 26 start-page: 100,642 year: 2019 article-title: Estimating hydrologic vulnerabilities to climate change using simulated historical data: A proof‐of‐concept for a rapid assessment algorithm in the colorado river basin publication-title: Journal of Hydrology: Regional Studies – start-page: 1025) year: 1994 – volume: 47 year: 2011 article-title: Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development publication-title: Water Resources Research – volume: 519 start-page: 1031 year: 2014 end-page: 1041 article-title: An empirical method for approximating stream baseflow time series using groundwater table fluctuations publication-title: Journal of Hydrology – volume: 41 start-page: 399 issue: 3 year: 1996 end-page: 417 article-title: Artificial neural networks as rainfall‐runoff models publication-title: Hydrological Sciences Journal – volume: 47 start-page: 74 issue: 11 year: 2014 end-page: 78 article-title: Theory‐guided data science for climate change publication-title: Computer – volume: 41 start-page: 4065 issue: 10 year: 2016 end-page: 4076 article-title: Rainfall‐runoff modeling using support vector machine in snow‐affected watershed publication-title: Arabian Journal for Science and Engineering – volume: 1173A start-page: 158 year: 1999 article-title: Environmental characteristics and water quality of Hydrologic Benchmark Network stations in the eastern united states publication-title: U.S. Geological Survey Circular – year: 2013 – volume: 55 start-page: 2215 issue: 7 year: 2017 end-page: 2227 article-title: Machine‐learning‐augmented predictive modeling of turbulent separated flows over airfoils publication-title: AIAA Journal – year: 2009 – volume: NCAR Tech. Note NCAR/TN‐5141STR year: 2015 – volume: 51 start-page: 2498 issue: 4 year: 2015 end-page: 2514 article-title: A unified approach for process‐based hydrologic modeling: 1. Modeling concept publication-title: Water Resources Research – start-page: 443 year: 1995 end-page: 476 – volume: 529 start-page: 1060 year: 2015 end-page: 1069 article-title: Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers publication-title: Journal of Hydrology – volume: 95 start-page: 363 issue: 1 year: 2013 end-page: 380 article-title: Incorporating basic hydrological concepts into genetic programming for rainfall‐runoff forecasting publication-title: Computing – start-page: 627 year: 1995 end-page: 668) – volume: 19 start-page: 209 year: 2015 end-page: 223 article-title: Development of a large‐sample watershed‐scale hydrometeorological dataset for the contiguous USA: Dataset characteristics and assessment of regional variability in hydrologic model performance publication-title: Hydrology and Earth System Sciences – volume: 32 start-page: 3207 issue: 11 year: 2018 end-page: 3225 article-title: Non‐crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes publication-title: Stochastic Environmental Research and Risk Assessment – start-page: 42 year: 2001 end-page: 49 – volume: 85 start-page: 279 issue: 3 year: 2007 end-page: 294 article-title: Artificial neural network forecasting of nonlinear Markov processes publication-title: Canadian Journal of Physics – volume: 3 start-page: p.e1602614 issue: 4 year: 2017 article-title: Data‐driven discovery of partial differential equations publication-title: Science Advances – start-page: 183 year: 2014 end-page: 199 – volume: 5 start-page: 124 issue: 2 year: 2000 end-page: 137 article-title: Artificial neural networks in hydrology. II: Hydrologic applications publication-title: Journal of Hydrologic Engineering – start-page: 1 year: 2012 end-page: 22 – volume: 540 start-page: 623 year: 2016 end-page: 640 article-title: A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network publication-title: Journal of Hydrology – volume: 20 start-page: 2611 issue: 7 year: 2016 end-page: 2628 article-title: Machine learning methods for empirical streamflow simulation: A comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds publication-title: Hydrology and Earth System Sciences – volume: 19 start-page: 15990 year: 2017a – start-page: 64 year: 2001 – volume: 25 start-page: 523 issue: 4 year: 2010 end-page: 543 article-title: Hydrological field data from a modeller's perspective: Part 2: Process‐based evaluation of model hypotheses publication-title: Hydrological Processes – volume: 397 start-page: 1 issue: 1 year: 2011 end-page: 9 article-title: Testing the structure of a hydrological model using genetic programming publication-title: Journal of Hydrology – volume: 113 start-page: 3932 issue: 15 year: 2016 end-page: 3937 article-title: Discovering governing equations from data by sparse identification of nonlinear dynamical systems publication-title: In Proceedings of the National Academy of Sciences – volume: 52 start-page: 954 year: 2016 end-page: 989 article-title: From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions publication-title: Water Resources Research – volume: 142 start-page: 4,015,050 issue: 2 year: 2015 article-title: Curses, tradeoffs, and scalable management: Advancing evolutionary multiobjective direct policy search to improve water reservoir operations publication-title: Journal of Water Resources Planning and Management – volume: 29 start-page: 2318 issue: 10 year: 2017 end-page: 2331 article-title: Theory‐guided data science: A new paradigm for scientific discovery from data publication-title: IEEE Transactions on Knowledge and Data Engineering – volume: 566 start-page: 643 year: 2018 end-page: 667 article-title: Genetic programming in water resources engineering: A state‐of‐the‐art review publication-title: Journal of Hydrology – volume: 62 start-page: 661 issue: 5 year: 2012 end-page: 669 article-title: Artificial neural networks as routine for error correction with an application in singapore regional model publication-title: Ocean Dynamics – volume: 51 start-page: 502 issue: 2 year: 2014 end-page: 5125 article-title: Development and operational testing of a super‐ensemble artificial intelligence flood‐forecast model for a Pacific Northwest River publication-title: JAWRA Journal of the American Water Resources Association – volume: 47 year: 2011 article-title: Elements of a flexible approach for conceptual hydrological modeling: 2. Application and experimental insights publication-title: Water Resources Research – year: 2015 – volume: 28 start-page: 2451 issue: 4 year: 2014 end-page: 2467 article-title: Catchment properties, function, and conceptual model representation: Is there a correspondence? publication-title: Hydrological Processes – volume: 11 start-page: 237 issue: 3‐4 year: 2009 end-page: 251 article-title: Investigating the capabilities of evolutionary data‐driven techniques using the challenging estimation of soil moisture content publication-title: Journal of Hydroinformatics – volume: 4 start-page: 171 issue: 3 year: 1973 end-page: 190 article-title: Numerical simulation of the rainfall‐runoff process on a daily basis publication-title: Hydrology Research – volume: 525 start-page: 711 year: 2015 end-page: 723 article-title: Development of a modular streamflow model to quantify runoff contributions from different land uses in tropical urban environments using genetic programming publication-title: Journal of Hydrology – volume: 279 start-page: 275 issue: 1 year: 2003 end-page: 289 article-title: Improvement of a parsimonious model for streamflow simulation publication-title: Journal of Hydrology – volume: 5 start-page: 13 issue: 1 year: 2001 end-page: 26 article-title: A framework for development and application of hydrological models publication-title: Hydrology and Earth System Sciences Discussions – volume: 44 year: 2008 article-title: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models publication-title: Water Resources Research – volume: 12 start-page: 891 issue: 3 year: 2018 end-page: 905 article-title: Improving gridded snow water equivalent products in British Columbia, Canada: Multi‐source data fusion by neural network models publication-title: The Cryosphere – start-page: 2175 year: 2019 end-page: 2199 article-title: Making the black box more transparent: Understanding the physical implications of machine learning publication-title: Bulletin of the American Meteorological Society – start-page: 35 year: 2006 end-page: 42) – volume: 6 start-page: 182 issue: 2 year: 2002 end-page: 197 article-title: A fast and elitist multiobjective genetic algorithm: NSGA‐II publication-title: IEEE Transactions on Evolutionary Computation – volume: 11 start-page: 181 issue: 3‐4 year: 2009 end-page: 193 article-title: Introducing knowledge into learning based on genetic programming publication-title: Journal of Hydroinformatics – volume: 51 start-page: 7777 year: 2015 end-page: 7784 article-title: Do we need a community hydrological model? publication-title: Water Resources Research – volume: 255 start-page: 123 issue: 1–4 year: 2002 end-page: 133 article-title: Estimation of missing streamflow data using principles of chaos theory publication-title: Journal of Hydrology – volume: 11 start-page: 1563 issue: 5 year: 2007 end-page: 1579 article-title: Neural network modelling of non‐linear hydrological relationships publication-title: Hydrology and Earth System Sciences Discussions – volume: 2 year: 1995 – volume: 6 start-page: 278 year: 1992 end-page: 298 article-title: The future of distributed models: Model calibration and uncertainty prediction publication-title: Hydrological Processes – volume: 22 start-page: 1687 issue: 13 year: 2002 end-page: 1708 article-title: A graphical sensitivity analysis for statistical climate models: Application to indian monsoon rainfall prediction by artificial neural networks and multiple linear regression models publication-title: International Journal of Climatology – volume: 10 start-page: 282 issue: 3 year: 1970 end-page: 290 article-title: River flow forecasting through conceptual models Part I—A discussion of principles publication-title: Journal of hydrology – volume: 8 start-page: 348 issue: 6 year: 2014 end-page: 354 article-title: Genetic programming: Principles, applications and opportunities for hydrological modelling publication-title: World Academy of Science, Engineering and Technology International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering – volume: 3 start-page: 41 year: 2002 end-page: 79 article-title: Declarative and preferential bias in GP‐based scientific discovery publication-title: Genetic Programming and Evolvable Machines – start-page: 281 year: 2015 end-page: 285 – year: 2017 – volume: 4 start-page: 831 year: 1992 end-page: 838 article-title: Principles of risk minimization for learning theory publication-title: Advances in Neural Information Processing Systems – year: 1999 – ident: e_1_2_10_58_1 – volume: 1173 start-page: 158 year: 1999 ident: e_1_2_10_63_1 article-title: Environmental characteristics and water quality of Hydrologic Benchmark Network stations in the eastern united states publication-title: U.S. Geological Survey Circular – ident: e_1_2_10_86_1 doi: 10.1016/j.jhydrol.2010.11.009 – ident: e_1_2_10_43_1 doi: 10.1109/ACCESS.2019.2936989 – ident: e_1_2_10_33_1 doi: 10.2166/hydro.2009.032 – ident: e_1_2_10_13_1 doi: 10.1002/hyp.3360060305 – ident: e_1_2_10_75_1 doi: 10.1007/s11269-009-9414-5 – ident: e_1_2_10_48_1 doi: 10.1061/(ASCE)1084-0699(2000)5:2(124) – ident: e_1_2_10_77_1 doi: 10.1016/S0022-1694(03)00225-7 – ident: e_1_2_10_89_1 doi: 10.5194/tc-12-891-2018 – ident: e_1_2_10_40_1 doi: 10.5194/hess-10-139-2006 – ident: e_1_2_10_61_1 doi: 10.5194/adgeo-5-89-2005 – ident: e_1_2_10_65_1 doi: 10.1175/BAMS-D-18-0195.1 – ident: e_1_2_10_20_1 doi: 10.1002/joc.811 – ident: e_1_2_10_87_1 doi: 10.5194/hess-20-2611-2016 – ident: e_1_2_10_93_1 doi: 10.5194/hessd-10-5457-2013 – start-page: 627 volume-title: Computer models of watershed hydrology year: 1995 ident: e_1_2_10_14_1 – ident: e_1_2_10_62_1 doi: 10.2166/nh.2015.221 – ident: e_1_2_10_19_1 doi: 10.1007/s00477-018-1573-6 – ident: e_1_2_10_27_1 doi: 10.1002/hyp.7072 – ident: e_1_2_10_46_1 doi: 10.2166/hydro.2006.020b – ident: e_1_2_10_102_1 – ident: e_1_2_10_24_1 doi: 10.1002/2015WR017198 – ident: e_1_2_10_38_1 doi: 10.1002/hyp.9726 – ident: e_1_2_10_22_1 doi: 10.2166/hydro.2017.078 – ident: e_1_2_10_52_1 doi: 10.1016/j.jhydrol.2016.06.026 – ident: e_1_2_10_53_1 doi: 10.1080/09715010.2015.1103201 – ident: e_1_2_10_42_1 doi: 10.1111/jawr.12259 – ident: e_1_2_10_68_1 doi: 10.1016/j.jhydrol.2015.04.032 – ident: e_1_2_10_95_1 doi: 10.1007/978-1-4757-2440-0 – ident: e_1_2_10_32_1 doi: 10.5194/hess-14-1931-2010 – ident: e_1_2_10_45_1 doi: 10.1061/(ASCE)WR.1943-5452.0000570 – ident: e_1_2_10_2_1 doi: 10.5194/hess-11-1563-2007 – ident: e_1_2_10_9_1 – ident: e_1_2_10_39_1 doi: 10.1002/2015WR017398 – ident: e_1_2_10_30_1 doi: 10.1007/978-3-319-09054-2_58 – start-page: 343 volume-title: 5th australian joint conference on artificial intelligence year: 1992 ident: e_1_2_10_78_1 – ident: e_1_2_10_81_1 – ident: e_1_2_10_54_1 doi: 10.1109/TKDE.2017.2720168 – ident: e_1_2_10_55_1 doi: 10.1109/TKDE.2018.2861006 – ident: e_1_2_10_8_1 doi: 10.1061/40569(2001)64 – ident: e_1_2_10_69_1 doi: 10.1080/02626669609491511 – ident: e_1_2_10_34_1 doi: 10.1016/S0022-1694(01)00513-3 – volume: 188 start-page: 1 issue: 44 year: 2016 ident: e_1_2_10_70_1 article-title: GIS‐based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran publication-title: Environmental Monitoring and Assessment – ident: e_1_2_10_17_1 doi: 10.1073/pnas.1517384113 – ident: e_1_2_10_67_1 doi: 10.1016/j.jhydrol.2014.08.033 – volume: 4 start-page: 831 year: 1992 ident: e_1_2_10_94_1 article-title: Principles of risk minimization for learning theory publication-title: Advances in Neural Information Processing Systems – ident: e_1_2_10_80_1 doi: 10.3808/jei.201400285 – ident: e_1_2_10_97_1 – ident: e_1_2_10_21_1 – start-page: 1999 volume-title: Proceedings of the 7th annual conference on genetic and evolutionary computation year: 2005 ident: e_1_2_10_7_1 – ident: e_1_2_10_83_1 doi: 10.1126/sciadv.1602614 – ident: e_1_2_10_31_1 doi: 10.1080/02626669909492221 – ident: e_1_2_10_44_1 doi: 10.1016/j.jhydrol.2015.09.028 – ident: e_1_2_10_103_1 doi: 10.2166/hydro.2004.0016 – ident: e_1_2_10_73_1 doi: 10.5194/hess-19-209-2015 – ident: e_1_2_10_15_1 doi: 10.1016/j.jhydrol.2013.11.007 – ident: e_1_2_10_5_1 doi: 10.2166/hydro.2000.0004 – ident: e_1_2_10_36_1 doi: 10.1016/j.jher.2013.03.005 – ident: e_1_2_10_37_1 doi: 10.1029/2010WR010174 – ident: e_1_2_10_85_1 doi: 10.1007/s13369-016-2095-5 – ident: e_1_2_10_56_1 doi: 10.1029/2011WR010748 – ident: e_1_2_10_99_1 doi: 10.1016/j.jhydrol.2020.124700 – volume: 8 start-page: 348 issue: 6 year: 2014 ident: e_1_2_10_76_1 article-title: Genetic programming: Principles, applications and opportunities for hydrological modelling publication-title: World Academy of Science, Engineering and Technology International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering – ident: e_1_2_10_88_1 doi: 10.2514/1.J055595 – ident: e_1_2_10_96_1 doi: 10.1016/j.geoderma.2017.03.013 – volume: 9 start-page: 1 issue: 20 year: 2016 ident: e_1_2_10_16_1 article-title: Uncertainty analysis of HEC‐HMS model using the glue method for flash flood forecasting of Mekerra watershed, Algeria publication-title: Arabian Journal of Geosciences – volume-title: Genetic programming: on the programming of computers by means of natural selection year: 1992 ident: e_1_2_10_60_1 – ident: e_1_2_10_49_1 doi: 10.1016/j.jhydrol.2009.08.003 – ident: e_1_2_10_10_1 – ident: e_1_2_10_6_1 doi: 10.2166/nh.2002.0012 – ident: e_1_2_10_72_1 doi: 10.1007/s11269-006-4007-z – ident: e_1_2_10_35_1 doi: 10.1109/MC.2014.335 – ident: e_1_2_10_101_1 – volume-title: Parallel distributed processing: Explorations in the microstructure of cognition year: 1986 ident: e_1_2_10_64_1 – volume-title: Genetic algorithms in search, optimization, and machine learning year: 1989 ident: e_1_2_10_47_1 – ident: e_1_2_10_51_1 doi: 10.1017/CBO9780511627217 – ident: e_1_2_10_82_1 doi: 10.1029/2011WR010643 – ident: e_1_2_10_91_1 doi: 10.1080/02626667909491876 – ident: e_1_2_10_26_1 doi: 10.1029/2007WR006735 – ident: e_1_2_10_3_1 doi: 10.2166/hydro.2009.041 – ident: e_1_2_10_57_1 doi: 10.1023/A:1014596120381 – ident: e_1_2_10_100_1 doi: 10.1002/2014WR016731 – ident: e_1_2_10_79_1 – ident: e_1_2_10_90_1 doi: 10.1016/j.ejrh.2019.100642 – ident: e_1_2_10_92_1 doi: 10.1007/s10236-012-0524-x – start-page: 443 volume-title: Computer Models of Watershed Hydrology year: 1995 ident: e_1_2_10_11_1 – ident: e_1_2_10_74_1 doi: 10.2166/nh.1973.0013 – ident: e_1_2_10_23_1 doi: 10.1002/hyp.7902 – ident: e_1_2_10_18_1 doi: 10.1002/hyp.7936 – ident: e_1_2_10_28_1 doi: 10.1007/978-3-319-07494-8_13 – ident: e_1_2_10_41_1 doi: 10.1139/p07-037 – ident: e_1_2_10_66_1 doi: 10.1016/j.jhydrol.2018.09.043 – ident: e_1_2_10_50_1 doi: 10.1007/s00607-013-0298-0 – ident: e_1_2_10_84_1 doi: 10.1002/0470848944.hsa016 – ident: e_1_2_10_71_1 doi: 10.1016/0022-1694(70)90255-6 – volume-title: Coping with floods: Lessons learned from recent experiences year: 1999 ident: e_1_2_10_4_1 – ident: e_1_2_10_12_1 doi: 10.1002/9781119951001.ch1 – ident: e_1_2_10_25_1 – ident: e_1_2_10_29_1 doi: 10.1109/4235.996017 – ident: e_1_2_10_59_1 doi: 10.1111/j.1752-1688.2001.tb00980.x – ident: e_1_2_10_98_1 doi: 10.5194/hess-5-13-2001
SSID	ssj0014567
Score	2.6027858
Snippet	Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded...
SourceID	proquest crossref wiley
SourceType	Aggregation Database Enrichment Source Index Database Publisher
SubjectTerms	Alabama Algorithms Atmospheric conditions Atmospheric models Blackwater rivers Catchment scale Catchments Computation Configurations Evolutionary algorithms Fieldwork flexible conceptual modeling framework Genetic algorithms genetic programming Hydrologic models Hydrology Learning algorithms Machine learning Modelling Neural networks Rain Rainfall Rainfall-runoff relationships rainfall‐runoff modeling River basins Rivers Runoff time series analysis Water resources Water resources research Watersheds
Title	Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction
URI	https://onlinelibrary.wiley.com/doi/abs/10.1029%2F2019WR026933 https://www.proquest.com/docview/2394892307 https://www.proquest.com/docview/2675577450
Volume	56
WOSCitedRecordID	wos000538987800013&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 1944-7973 dateEnd: 20231212 omitProxy: false ssIdentifier: ssj0014567 issn: 0043-1397 databaseCode: WIN dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1944-7973 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014567 issn: 0043-1397 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB6hggQX3ogtpTISnCCiieOszW0prIpUVlXUanuLHD-gYkmqfSDtjR_Agd_IL2HGcZblABLiFiXjJEo8_mY8M98APM1qzhG48wSxoEhy41SieCoSr3RRSLTIvQ2U-cfDyUSen6uTuOFGtTAdP8Rmw400I6zXpOC6XkSyAeLIRORS0xJdCHTJcQlO86CX03eTTRABbYNhH2AmQyfmvePwl9uDf0ekX2bmtrEa0GZ863_f8zbcjHYmG3UT4w5ccc1duN6XIS_wOLY__7i-B9-O1nber4KzNetqlJxl70OqpWORhfUDw_OMQkIe5X58_V6umtZ7Rg3VqKz9FRsx4rHGZ7KTLvHrM55GwdcIlpadtu3s08Uy3GW0WraBLrYbzaiHSKixuA9n47enh0dJbNOQaAQzkUhlbFoU1giXZoiE5kAYnUptRc1r7YTJnKJe1lzkWvmhy6ww1igj0YHNZCH5A9hp2sY9BEZlwOhgydprnzvJa281N9IZb9D1ys0Anve_qjKRw5xaacyqEEvPVLX9tQfwbCN92XF3_EFur__rVdTgRUUt46WiNPkBPNlcRt2jgIpuXLtCGfS2BNrP4mAAL8Ic-Otzqml5WGa54GL338QfwY2M3PyQMLQHO8v5yj2Ga-bL8mIx34erb8rx2fF-mPw_AexMBC4
linkProvider	Wiley-Blackwell
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwEB5BF2n3whtRWMBIcIJot0mc2tzKQlVEt1pFXe3eIscPWG1JUB9IvfEDOPAb-SXMOE4pB5AQt8gZ21Hs8cx4Zr4BeBaXSYKCO41QFmRRqq2MZNLjkZMqywRq5M54yPxxfzIR5-fyJNQ5pVyYBh9ic-FGnOHPa2JwupAOaAMEkomiS57laEOgTX4VdlLcSbwDO2_y4el440hA_aDfOplJ2Qmx7zjCwXb_36XSL1VzW2H1Emd447-_9SZcD8omGzS74xZcsdVt2G1zkRf4HGqgf1zfgW-jtZm3R-FszZpEJWvYsY-3tCxAsX5g2M7IL-SQ7sfX7_mqqp1jVFWNcttfsQEjMGuck5000V-fsBkJX6PENGxa17PLi6UfZbBa1h4ztunNqJCIT7S4C6fDt9OjURRqNUQKJRqPhNSml2VGc9uLURzqQ65VTyjDy6RUluvYSiponfBUSde3seHaaKkFWrGxyERyDzpVXdn7wCgXGK0sUTrlUiuS0hmVaGG102h_pboLL9q1KnQAMqd6GrPCO9RjWWz_7S4831B_bgA8_kC33y57Edh4UVDdeCEpVr4LTzevkQHJq6IqW6-QBk0ujko0P-zCS78J_jpPcZYf5XHKE_7g38ifwO5oejwuxu8m7x_CXkx2v48g2ofOcr6yj-Ca_rK8WMwfBx74CSd5B9o
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5Bi2gvvKtuacFIcIKIbhxnbW7bx6qIZbWKWrW3yPGjrbpNqn1U2hs_gAO_kV_C2HG2ywEkxC1yxkkUezIzmZnvA3gbF5Si4U4itAVplCgjIkHbLLJCpilHj9xqD5nf7wwG_OxMDAPPqeuFqfEhFj_cnGb477VTcHOjbUAbcCCZaLrEaYYxBMbk92E1YSJFzVw9yHon_UUiAf2DTpNkds5OqH3HK3xcnv-7VbpzNZcdVm9xeo__-1mfwKPgbJJuvTuewj1TPoO1phd5gseBA_1i_hy-H831uPkUjuakblQymnz19ZaGBCjWc4LjxOWFLMr9_PYjm5WVtcSxqrne9k-kSxyYNd6TDOvqr2scRsE9tJiaHFfV6Opy6q_SnU0rjxlbzyaOSMQ3WryAk97h8f5RFLgaIokWjUVcKN1OU62YacdoDtUuU7LNpWYFLaRhKjbCEVpTlkhhOybWTGklFMcoNuYppxuwUlal2QTieoExyuKFlTYxnBZWS6q4UVZh_JWoFrxv1ipXAcjc8WmMcp9Qj0W-_LZb8G4hfVMDePxBbrtZ9jyo8SR3vPFcuFr5FrxZnEYFdFkVWZpqhjIYcjF0otluCz74TfDX--Sn2X4WJ4yyrX8Tfw0Phwe9vP958OUlrMcu7PcFRNuwMh3PzA48ULfTy8n4VVCBX6XBB1U
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Hydrologically+Informed+Machine+Learning+for+Rainfall%E2%80%90Runoff+Modeling%3A+A+Genetic+Programming%E2%80%90Based+Toolkit+for+Automatic+Model+Induction&rft.jtitle=Water+resources+research&rft.au=Chadalawada%2C+Jayashree&rft.au=Herath%2C+H.+M.+V.+V.&rft.au=Babovic%2C+Vladan&rft.date=2020-04-01&rft.issn=0043-1397&rft.eissn=1944-7973&rft.volume=56&rft.issue=4&rft_id=info:doi/10.1029%2F2019WR026933&rft.externalDBID=n%2Fa&rft.externalDocID=10_1029_2019WR026933
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0043-1397&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0043-1397&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0043-1397&client=summon