Soil water erosion susceptibility assessment using deep learning algorithms

•Soil water erosion (SWE) is predicted though three kinds of deep learning algorithms.•Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) are investigated.•All three models had good prediction performance, with RNN being marginally the most superior....

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Journal of hydrology (Amsterdam) Ročník 618; s. 129229
Hlavní autori:	Khosravi, Khabat, Rezaie, Fatemeh, Cooper, James R., Kalantari, Zahra, Abolfathi, Soroush, Hatamiafkoueieh, Javad
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.03.2023
Predmet:	CNN Deep learning ecosystems erosion control infrastructure Land degradation LSTM neural networks prediction RNN Soil erosion soil water water erosion water quality watersheds Deep learning CNN Soil erosion RNN LSTM Land degradation
ISSN:	0022-1694, 1879-2707, 1879-2707
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	•Soil water erosion (SWE) is predicted though three kinds of deep learning algorithms.•Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) are investigated.•All three models had good prediction performance, with RNN being marginally the most superior.•Elevation was the most effective variable on soil water erosion susceptibility.•Maps of SWE susceptibility revealed that almost 40% of the catchment was highly or very highly susceptible to SWE. Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem services, water quality, flooding and infrastructure. Deep learning algorithms have been gaining attention in geoscience due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of soil erosion susceptibility is lacking. This study provides the first quantification of this potential. Spatial predictions of susceptibility are made using three deep learning algorithms – Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) – for an Iranian catchment that has historically experienced severe water erosion. Through a comparison of their predictive performance and an analysis of the driving geo-environmental factors, the results reveal: (1) elevation was the most effective variable on SWE susceptibility; (2) all three developed models had good prediction performance, with RNN being marginally the most superior; (3) maps of SWE susceptibility revealed that almost 40 % of the catchment was highly or very highly susceptible to SWE and 20 % moderately susceptible, indicating the critical need for soil erosion control in this catchment. Through these algorithms, the soil erosion susceptibility of catchments can potentially be predicted accurately and with ease using readily available data. Thus, the results reveal that these models have great potential for use in data poor catchments, such as the one studied here, especially in developing nations where technical modeling skills and understanding of the erosion processes occurring in the catchment may be lacking.
AbstractList	Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem services, water quality, flooding and infrastructure. Deep learning algorithms have been gaining attention in geoscience due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of soil erosion susceptibility is lacking. This study provides the first quantification of this potential. Spatial predictions of susceptibility are made using three deep learning algorithms – Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) – for an Iranian catchment that has historically experienced severe water erosion. Through a comparison of their predictive performance and an analysis of the driving geo-environmental factors, the results reveal: (1) elevation was the most effective variable on SWE susceptibility; (2) all three developed models had good prediction performance, with RNN being marginally the most superior; (3) maps of SWE susceptibility revealed that almost 40 % of the catchment was highly or very highly susceptible to SWE and 20 % moderately susceptible, indicating the critical need for soil erosion control in this catchment. Through these algorithms, the soil erosion susceptibility of catchments can potentially be predicted accurately and with ease using readily available data. Thus, the results reveal that these models have great potential for use in data poor catchments, such as the one studied here, especially in developing nations where technical modeling skills and understanding of the erosion processes occurring in the catchment may be lacking. •Soil water erosion (SWE) is predicted though three kinds of deep learning algorithms.•Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) are investigated.•All three models had good prediction performance, with RNN being marginally the most superior.•Elevation was the most effective variable on soil water erosion susceptibility.•Maps of SWE susceptibility revealed that almost 40% of the catchment was highly or very highly susceptible to SWE. Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem services, water quality, flooding and infrastructure. Deep learning algorithms have been gaining attention in geoscience due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of soil erosion susceptibility is lacking. This study provides the first quantification of this potential. Spatial predictions of susceptibility are made using three deep learning algorithms – Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) – for an Iranian catchment that has historically experienced severe water erosion. Through a comparison of their predictive performance and an analysis of the driving geo-environmental factors, the results reveal: (1) elevation was the most effective variable on SWE susceptibility; (2) all three developed models had good prediction performance, with RNN being marginally the most superior; (3) maps of SWE susceptibility revealed that almost 40 % of the catchment was highly or very highly susceptible to SWE and 20 % moderately susceptible, indicating the critical need for soil erosion control in this catchment. Through these algorithms, the soil erosion susceptibility of catchments can potentially be predicted accurately and with ease using readily available data. Thus, the results reveal that these models have great potential for use in data poor catchments, such as the one studied here, especially in developing nations where technical modeling skills and understanding of the erosion processes occurring in the catchment may be lacking.
ArticleNumber	129229
Author	Hatamiafkoueieh, Javad Khosravi, Khabat Cooper, James R. Kalantari, Zahra Rezaie, Fatemeh Abolfathi, Soroush
Author_xml	– sequence: 1 givenname: Khabat surname: Khosravi fullname: Khosravi, Khabat email: khabat.khosravi@gmail.com, kkhosrav@fiu.edu organization: Department of Earth and Environment, Florida International University, Miami, USA – sequence: 2 givenname: Fatemeh surname: Rezaie fullname: Rezaie, Fatemeh email: rezaie@kigam.re.kr organization: Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro, Yuseong-gu, Daejeon 34132, Republic of Korea – sequence: 3 givenname: James R. surname: Cooper fullname: Cooper, James R. email: james.cooper@liverpool.ac.uk organization: Department of Geography & Planning, School of Environmental Sciences, University of Liverpool, Liverpool, UK – sequence: 4 givenname: Zahra orcidid: 0000-0002-7978-0040 surname: Kalantari fullname: Kalantari, Zahra email: zahrak@kth.se organization: Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH Royal Institute of Technology, Stockholm, Sweden – sequence: 5 givenname: Soroush orcidid: 0000-0001-7319-4289 surname: Abolfathi fullname: Abolfathi, Soroush email: Soroush.Abolfathi@warwick.ac.uk organization: School of Engineering, University of Warwick, CV4 7AL Coventry, UK – sequence: 6 givenname: Javad surname: Hatamiafkoueieh fullname: Hatamiafkoueieh, Javad email: khatamiafkuiekh-d@rudn.ru organization: Department of Mechanics and Control Processes, Academy of Engineering, Peoples' Friendship University of Russia (RUDN University), Miklukho-Maklaya Str. 6, Moscow 117198, Russian Federation
BackLink	https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-325309$$DView record from Swedish Publication Index (Kungliga Tekniska Högskolan)
BookMark	eNqFkEtPHDEQhC1EJJbHT0CaI5fZ-DUv5YAQJAEFiUMQV8v29O724h0Ptido_328GnLJhb60WqoqVX-n5HjwAxByyeiSUVZ_3S63m30fvFtyysWS8Y7z7ogsWNt0JW9oc0wWlHJesrqTJ-Q0xi3NI4RckF-_PbriXScIBQQf0Q9FnKKFMaFBh2lf6Bghxh0MqZgiDuuiBxgLBzoMh0u7tQ-YNrt4Tr6stItw8bHPyPOP78-39-Xj08-H25vHUsuKprLqmxXntdS6hg44E1a3tqmZaU3u1xphmG1E1xrZUGOhFlCZSveVpVYabcUZKefY-A7jZNQYcKfDXnmN6g5fbpQPa_WaNkrwStAu669m_Rj82wQxqR3mB53TA_gpKt4KyWknZZWl32apzShigJWymHTKUFLQ6BSj6kBcbdUHcXUgrmbi2V395_5X7TPf9eyDDO0PQlDRIgwWegxgk-o9fpLwF66tom8
CitedBy_id	crossref_primary_10_1007_s11069_024_06887_5 crossref_primary_10_1016_j_ijsrc_2024_08_003 crossref_primary_10_1016_j_jenvman_2023_118403 crossref_primary_10_1109_ACCESS_2025_3561137 crossref_primary_10_1016_j_clet_2025_100984 crossref_primary_10_1016_j_jenvman_2023_118368 crossref_primary_10_1016_j_oceaneng_2023_115583 crossref_primary_10_1016_j_engappai_2023_107613 crossref_primary_10_1109_ACCESS_2025_3582906 crossref_primary_10_48077_scihor12_2023_76 crossref_primary_10_1016_j_rineng_2025_104254 crossref_primary_10_1016_j_engappai_2024_108156 crossref_primary_10_1016_j_jhydrol_2025_134004 crossref_primary_10_1016_j_ecolind_2025_113340 crossref_primary_10_1002_hyp_70138 crossref_primary_10_1016_j_heliyon_2024_e33082 crossref_primary_10_3390_agronomy14112467 crossref_primary_10_1007_s00477_025_03042_9 crossref_primary_10_1007_s11069_023_06357_4 crossref_primary_10_1016_j_jwpe_2023_104303 crossref_primary_10_1109_ACCESS_2024_3463871 crossref_primary_10_1016_j_chemosphere_2024_141394 crossref_primary_10_3390_app15063139 crossref_primary_10_1002_ldr_70152 crossref_primary_10_1007_s13197_025_06428_3 crossref_primary_10_1109_ACCESS_2024_3351754 crossref_primary_10_1016_j_jwpe_2024_104778 crossref_primary_10_3390_w15071437 crossref_primary_10_1007_s11629_025_9645_9 crossref_primary_10_3390_su152115374 crossref_primary_10_3390_w15142674 crossref_primary_10_1016_j_jenvman_2025_126214 crossref_primary_10_1016_j_rineng_2025_104674 crossref_primary_10_1029_2023JD039538 crossref_primary_10_1016_j_jwpe_2023_103957 crossref_primary_10_1016_j_ecolind_2024_112915 crossref_primary_10_1016_j_rsase_2025_101498 crossref_primary_10_2166_hydro_2023_216 crossref_primary_10_1038_s41598_023_32343_8 crossref_primary_10_2166_hydro_2023_327 crossref_primary_10_1016_j_jenvman_2023_119589 crossref_primary_10_1016_j_jhydrol_2024_130719 crossref_primary_10_1016_j_jwpe_2024_104789 crossref_primary_10_1016_j_landusepol_2025_107592 crossref_primary_10_1186_s40068_025_00402_w crossref_primary_10_1002_gj_70070 crossref_primary_10_1007_s11368_023_03668_8 crossref_primary_10_1016_j_jwpe_2025_107158 crossref_primary_10_3390_rs16142595 crossref_primary_10_2166_wst_2023_340 crossref_primary_10_1038_s41598_024_55942_5 crossref_primary_10_1016_j_engappai_2024_108444 crossref_primary_10_1016_j_jwpe_2023_104757 crossref_primary_10_1016_j_heliyon_2023_e17620 crossref_primary_10_1109_TGRS_2023_3324043 crossref_primary_10_1111_ejss_70202 crossref_primary_10_2166_hydro_2025_287 crossref_primary_10_1016_j_jece_2023_110593 crossref_primary_10_1016_j_jwpe_2024_104876 crossref_primary_10_1016_j_jhydrol_2024_132269 crossref_primary_10_1016_j_jenvrad_2025_107667 crossref_primary_10_1016_j_rineng_2025_104606 crossref_primary_10_3390_su17052250 crossref_primary_10_1007_s10661_025_14401_y crossref_primary_10_1016_j_jag_2024_104305 crossref_primary_10_1016_j_uclim_2024_102272 crossref_primary_10_1109_ACCESS_2024_3369673 crossref_primary_10_1007_s11069_025_07482_y crossref_primary_10_3390_w17091351 crossref_primary_10_3390_su16020795 crossref_primary_10_3390_w16203009 crossref_primary_10_1016_j_catena_2025_108954 crossref_primary_10_1007_s00477_023_02637_4 crossref_primary_10_1016_j_ijsrc_2025_02_004 crossref_primary_10_1016_j_scitotenv_2023_166960 crossref_primary_10_1007_s41101_024_00324_1 crossref_primary_10_1007_s12040_024_02294_3 crossref_primary_10_1007_s10668_024_05285_y crossref_primary_10_1016_j_jhydrol_2024_132003 crossref_primary_10_1007_s12145_025_01995_7 crossref_primary_10_1029_2024WR039169 crossref_primary_10_1016_j_jhydrol_2024_130987 crossref_primary_10_1016_j_ejrh_2025_102285 crossref_primary_10_3390_rs15164067 crossref_primary_10_3390_su16208918 crossref_primary_10_1016_j_jclepro_2023_137689 crossref_primary_10_1080_10106049_2024_2367611 crossref_primary_10_1007_s10661_023_11796_4 crossref_primary_10_3389_fbuil_2024_1343398 crossref_primary_10_1016_j_ecolind_2025_113839 crossref_primary_10_1016_j_aeolia_2024_100924 crossref_primary_10_2478_johh_2024_0009 crossref_primary_10_1038_s41598_023_38447_5 crossref_primary_10_1016_j_engappai_2024_108238 crossref_primary_10_1016_j_jclepro_2023_140031 crossref_primary_10_1016_j_asoc_2024_112352 crossref_primary_10_1109_ACCESS_2023_3314335 crossref_primary_10_3390_agronomy15030596 crossref_primary_10_1038_s41598_024_70262_4 crossref_primary_10_1016_j_catena_2025_109388
Cites_doi	10.1155/2012/974638 10.1029/2005WR004468 10.1016/j.scitotenv.2017.04.046 10.1300/J411v11n01_04 10.1080/03736245.2020.1716838 10.1007/s12665-015-4648-4 10.3390/ijgi8120578 10.1002/hyp.9966 10.1016/j.agwat.2010.12.012 10.1016/S0012-8252(99)00046-X 10.1007/s12665-014-3523-z 10.1016/j.agee.2010.10.015 10.1016/j.compag.2016.01.026 10.1186/s41610-018-0075-2 10.1002/ldr.3794 10.2166/hydro.2013.234 10.1002/ldr.2560 10.1016/j.envsoft.2021.105285 10.1016/j.jenvman.2021.112206 10.3390/hydrology8040171 10.1016/j.iswcr.2020.09.005 10.1016/j.iswcr.2020.10.005 10.1016/j.jhydrol.2016.06.027 10.1016/j.jhydrol.2010.01.024 10.1007/s11069-007-9188-0 10.1007/s11269-014-0774-0 10.3390/agronomy11071426 10.1016/j.jclepro.2018.08.207 10.2478/jwld-2018-0002 10.1016/j.dsp.2021.103093 10.1007/s10668-012-9351-y 10.1016/j.eswa.2014.02.047 10.1016/j.geoderma.2016.09.002 10.1623/hysj.53.3.656 10.1080/1747423X.2011.560292 10.1080/17538947.2021.1919230 10.1007/s40333-015-0057-5 10.1016/j.scitotenv.2015.01.008 10.1061/(ASCE)0887-3801(2001)15:3(208) 10.1016/j.jhydrol.2020.125734 10.1016/j.jenvrad.2010.03.008 10.13031/2013.31195 10.1016/S0143-6228(97)00002-7 10.1016/j.geomorph.2010.06.011 10.13031/2013.27570 10.1016/j.geoderma.2014.11.025 10.13031/2013.27535 10.1016/j.measurement.2020.108066 10.1016/j.jenvman.2012.10.043 10.1016/j.catena.2016.03.015 10.1016/j.geomorph.2016.03.018 10.5194/hess-22-4771-2018 10.1016/j.jhydrol.2020.125552 10.1002/ldr.3058 10.3390/rs11172046 10.1016/j.jhydrol.2008.04.002 10.1016/j.jhydrol.2012.05.031 10.1002/ldr.4391 10.1111/j.1475-2743.2003.tb00320.x 10.1029/2017MS001270 10.1023/B:NHAZ.0000007172.62651.2b 10.1016/j.procs.2017.08.227 10.1177/0309133312444943 10.1002/esp.1624 10.1016/j.jhydrol.2020.125423 10.1016/0341-8162(92)90007-X 10.4137/ASWR.S3427 10.1017/S1355770X11000180 10.1016/j.asej.2020.09.015 10.1016/j.epsr.2022.108635 10.1016/j.gsf.2020.06.013 10.1007/s13244-018-0639-9 10.1080/19475705.2021.1994474 10.3390/land10040422 10.3390/w12071995 10.1016/j.aej.2022.03.008 10.3923/pjbs.2008.1893.1900 10.1046/j.1365-2664.2000.00491.x 10.1016/j.knosys.2018.08.036
ContentType	Journal Article
Copyright	2023 The Authors
Copyright_xml	– notice: 2023 The Authors
DBID	6I. AAFTH AAYXX CITATION 7S9 L.6 ADTPV AFDQA AOWAS D8T D8V ZZAVC
DOI	10.1016/j.jhydrol.2023.129229
DatabaseName	ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef AGRICOLA AGRICOLA - Academic SwePub SWEPUB Kungliga Tekniska Högskolan full text SwePub Articles SWEPUB Freely available online SWEPUB Kungliga Tekniska Högskolan SwePub Articles full text
DatabaseTitle	CrossRef AGRICOLA AGRICOLA - Academic
DatabaseTitleList	AGRICOLA
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography
EISSN	1879-2707
ExternalDocumentID	oai_DiVA_org_kth_325309 10_1016_j_jhydrol_2023_129229 S0022169423001713
GroupedDBID	--K --M -~X .~1 0R~ 1B1 1RT 1~. 1~5 29K 4.4 457 4G. 5GY 5VS 6I. 6TJ 7-5 71M 8P~ 9JM 9JN AABNK AABVA AACTN AAEDT AAEDW AAFTH AAIAV AAIKJ AAKOC AALCJ AALRI AAOAW AAQFI AAQXK AATLK AAXUO ABEFU ABFNM ABGRD ABJNI ABMAC ABQEM ABQYD ABTAH ABXDB ABYKQ ACDAQ ACGFS ACIUM ACLVX ACNCT ACRLP ACSBN ADBBV ADEZE ADMUD ADQTV AEBSH AEKER AENEX AEQOU AFFNX AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHHHB AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG ATOGT AVWKF AXJTR AZFZN BKOJK BLXMC CBWCG CS3 D-I DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FA8 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HLV HMA HVGLF HZ~ H~9 IHE IMUCA J1W K-O KOM LW9 LY3 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAB SCC SDF SDG SDP SEP SES SEW SPC SPCBC SPD SSA SSE SSZ T5K TN5 UQL VOH WUQ Y6R ZCA ZMT ZY4 ~02 ~G- ~KM 9DU AAHBH AATTM AAXKI AAYWO AAYXX ABUFD ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7S9 L.6 ADTPV AFDQA AOWAS D8T D8V ZZAVC
ID	FETCH-LOGICAL-a450t-5d7f2264aa6e9e213ca8c761b8b6948b3b1c7398b470bce63e5b5ad5c0c4bac3
ISICitedReferencesCount	115
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000942641000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0022-1694 1879-2707
IngestDate	Tue Nov 04 16:25:57 EST 2025 Mon Sep 29 06:40:48 EDT 2025 Tue Nov 18 22:42:24 EST 2025 Sat Nov 29 06:54:44 EST 2025 Sat Feb 24 15:50:02 EST 2024
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Deep learning CNN Soil erosion RNN LSTM Land degradation
Language	English
License	This is an open access article under the CC BY license.
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-a450t-5d7f2264aa6e9e213ca8c761b8b6948b3b1c7398b470bce63e5b5ad5c0c4bac3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ORCID	0000-0002-7978-0040 0000-0001-7319-4289
OpenAccessLink	https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-325309
PQID	2834209445
PQPubID	24069
ParticipantIDs	swepub_primary_oai_DiVA_org_kth_325309 proquest_miscellaneous_2834209445 crossref_citationtrail_10_1016_j_jhydrol_2023_129229 crossref_primary_10_1016_j_jhydrol_2023_129229 elsevier_sciencedirect_doi_10_1016_j_jhydrol_2023_129229
PublicationCentury	2000
PublicationDate	2023-03-01
PublicationDateYYYYMMDD	2023-03-01
PublicationDate_xml	– month: 03 year: 2023 text: 2023-03-01 day: 01
PublicationDecade	2020
PublicationTitle	Journal of hydrology (Amsterdam)
PublicationYear	2023
Publisher	Elsevier B.V
Publisher_xml	– name: Elsevier B.V
References	Anastasakis, Mort (b0050) 2001 Marques, Mora (b0265) 1992; 19 Panahi, Khosravi, Golkarian, Roostaei, Barzegar, Omidvar, Rezaie, Saco, Sharifi, Jun, Bateni, Lee, Lee (b0325) 2022; 1–23 Melesse, Ahmad, McClain, Wang, Lim (b0270) 2011; 98 Mohammadi, Balouei, Haji, Khaledi Darvishan, Karydas (b0280) 2021; 14 Raza, Ahrends, Habib-Ur-Rahman, Gaiser (b0350) 2021; 10 Wischmeier, W.H., Smith, D.D., 1965. Predicting rainfall-erosion losses from cropland east of the Rocky Mountains:guide for selection of practices for soil and water conservation. Agricultural Research Service, U. S. Dept of Agriculture in cooperation with Purdue Agricultural Experiment Station. Emadodin, Narita, Bork (b0160) 2012; 14 Wainwright, Parsons, Müller, Brazier, Powell, Fenti (b0440) 2008; 33 Abrahart, Anctil, Coulibaly, Dawson, Mount, See, Shamseldin, Solomatine, Toth, Wilby (b0010) 2012; 36 Thi Ngo, Panahi, Khosravi, Ghorbanzadeh, Kariminejad, Cerda, Lee (b0400) 2021; 12 Orgill, Condon, Conyers, Morris, Alcock, Murphy, Greene (b0310) 2018; 29 Phinzi, Ngetar, Ebhuoma (b0335) 2021; 103 Ghorbanzadeh, Meena, Blaschke, Aryal (b0190) 2019; 11 Tan, Leung, Li, Tesfa (b0390) 2018; 10 Williams, J.R., 1975. Sediment-yield prediction with Universal Equation using runoff energy factor. Afshar, Ayoubi, Jalalian (b0020) 2010; 101 Puigdefabregas, Sole, Gutierrez, del Barrio, Boer (b0340) 1999; 48 Sajedi-Hosseini, Choubin, Solaimani, Cerdà, Kavian (b0375) 2018; 29 Kinnell (b0230) 2017; 596–597 Ajitha, Goel, Assudani, Radhika, Goel (b0030) 2022; 212 . Ebtehaj, Bonakdari (b0150) 2014; 28 Emadodin, Bork (b0155) 2012; 7 Auerswald, Kainz, Fiener (b0070) 2006; 19 Solaimani, Hadian Amr (b0380) 2008; 11 Akbari, A.-M., 2017. Soil erosion in Iran 2.5 times the world average [WWW Document]. Tehran Times. URL https://www.tehrantimes.com/news/418381/Soil-erosion-in-Iran-2-5-times-the-world-average#:∼:text=TEHRAN — Iran has a mean, deputy agriculture minister has said. Evans (b0165) 1997; 17 Yousefi, Moradi, Boll, Schönbrodt-Stitt (b0485) 2016; 284 Rosskopf, Di Iorio, Circelli, Colombo, Aucelli (b0365) 2020; 8 Petroselli, Apollonio, Luca, Salvaneschi, Pecci, Marras, Schirone (b0330) 2021; 8 Wischmeier, W.H., Smith, D.D., 1978. Predicting rainfall erosion losses: a guide to conservation planning. Science and Education Administration, U.S. Dept. of Agriculture. Amiri (b0045) 2010; 3 Deng, Huang, Huang, Rozelle, Gibson (b0120) 2011; 16 Fang, Guo (b0170) 2015; 74 Angileri, Conoscenti, Hochschild, Märker, Rotigliano, Agnesi (b0055) 2016; 262 Goyal, Bharti, Quilty, Adamowski, Pandey (b0200) 2014; 41 Sternberg, Gutman, Perevolotsky, Ungar, Kigel (b0385) 2000; 37 Khosravi, Panahi, Tien Bui (b0215) 2018; 22 Gliessman (b0195) 2004; 11 Pally, Samadi (b0315) 2022; 148 Liu, Lang, Liu, Yan (b0260) 2019; 163 Yu, Rosewell (b0495) 1996; 39 Mutlu, Nefeslioglu, Sezer, Akcayol, Gokceoglu (b0290) 2019; 8 Tang, Xu, Bennett, Li (b0395) 2015; 73 Tien Bui, Pradhan, Nampak, Bui, Tran, Nguyen (b0410) 2016; 540 Yousefi, Pourghasemi, Avand, Janizadeh, Tavangar, Santosh (b0490) 2021; 32 Mirzabaev, Ahmed, Werner, Pender, Louhaichi (b0275) 2016; 8 Novakovic (b0305) 2009 World Bank, 2005. Islamic Republic of Iran, Cost Assessment of Environmental Degradation. Khalili Moghadam, Jabarifar, Bagheri, Shahbazi (b0210) 2015; 241–242 Vu, Tran, Cao, Tran, Hoang (b0435) 2020; 164 Robinson (b0360) 1998 Kisi, Dailr, Cimen, Shiri (b0235) 2012; 450–451 Wijitkosum (b0445) 2021; 9 Fang, Wang, Peng, Hong (b0175) 2021; 594 Dodangeh, Panahi, Rezaie, Lee, Tien Bui, Lee, Pradhan (b0130) 2020; 590 Cerdan, Govers, Le Bissonnais, Van Oost, Poesen, Saby, Gobin, Vacca, Quinton, Auerswald, Klik, Kwaad, Raclot, Ionita, Rejman, Rousseva, Muxart, Roxo, Dostal (b0085) 2010; 122 Ahmad, Reynolds, Rezgui (b0025) 2018; 203 Chen, Young (b0090) 2006; 42 Boudjemline, Semar (b0075) 2018; 36 Akhavan, Abedi-Koupai, Mousavi, Afyuni, Eslamian, Abbaspour (b0040) 2010; 139 Zhang, Nearing, Risse, McGregor (b0500) 1996; 39 Ebtehaj, Bonakdari (b0145) 2013; 7 Ganguli, Reddy (b0185) 2014; 28 Li, Xu, He, Fan, Yang, Li (b0255) 2021; 12 Çimen (b0100) 2008; 53 Donovan, Monaghan (b0135) 2021; 287 Chung, Fabbri (b0095) 2003; 30 Darvishan, Sadeghi, Gholami (b0115) 2010; 42 Abolhasani, Zehtabian, Khosravi, Rahmati, Alamdarloo, D'Odorico (b0005) 2022; 33 Tien Bui, Pradhan, Lofman, Revhaug (b0405) 2012; 2012 Laylin, D., 2018. Environmental and wildlife degradation in Iran [WWW Document]. Atl. Counc. URL Vaezi, Hasanzadeh, Cerdà (b0420) 2016; 142 Vojinovic, Abebe, Ranasinghe, Vacher, Martens, Mandl (b091) 2013; 15 Capolongo, Diodato, Mannaerts, Piccarreta, Strobl (b0080) 2008; 356 Nearing, Foster, Lane, Finkner (b0300) 1989; 32 Kinnell (b0225) 2010; 385 Narantsetseg, Kang, Ko (b0295) 2018; 42 Cooper, Wainwright, Parsons, Onda, Fukuwara, Obana, Kitchener, Long, Hargrave (b0110) 2012; 117 Dibike, Velickov, Solomatine, Abbott (b0125) 2001; 15 Du, Liu, Fu, Meng, Liu (b0140) 2022; 61 Yesilnacar (b0480) 2005 Aslam, Maqsoom, Salah Alaloul, Ali Musarat, Jabbar, Zafar (b0065) 2021; 12 Keshkamat, Tsendbazar, Zuidgeest, Shiirev-Adiya, van der Veen, van Maarseveen (b0205) 2013; 114 Panagos, Ballabio, Borrelli, Meusburger, Klik, Rousseva, Tadić, Michaelides, Hrabalíková, Olsen, Aalto, Lakatos, Rymszewicz, Dumitrescu, Beguería, Alewell (b0320) 2015; 511 Trabelsi, Meddouri, Maddouri (b0415) 2017; 112 Woolhiser, D.A., Smith, R.E., Goodrich, D.C., 1990. KINEROS:a kinematic runoff and erosion model: documentation and user manual. Rapp (b0345) 1963 Yamashita, Nishio, Do, Togashi (b0475) 2018; 9 Mosavi, Sajedi-Hosseini, Choubin, Taromideh, Rahi, Dineva (b0285) 2020; 12 Khosravi, Panahi, Golkarian, Keesstra, Saco, Bui, Lee (b0220) 2020; 591 Venkatappareddy, Culli, Srivastava, Lall (b0430) 2021; 115 Kisi, Genc, Dinc, Zounemat-Kermani (b0240) 2016; 122 Abuzaid, AbdelRahman, Fadl, Scopa (b0015) 2021; 11 Conoscenti, Di Maggio, Rotigliano (b0105) 2008; 46 Renard, K.G., Foster, G.R., Weesies, G.A., Mccool, D.K., Yoder, D.C., 1997. Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Sadeghi (b0370) 2017; 63 Vaezi (10.1016/j.jhydrol.2023.129229_b0420) 2016; 142 Puigdefabregas (10.1016/j.jhydrol.2023.129229_b0340) 1999; 48 Conoscenti (10.1016/j.jhydrol.2023.129229_b0105) 2008; 46 Fang (10.1016/j.jhydrol.2023.129229_b0170) 2015; 74 Narantsetseg (10.1016/j.jhydrol.2023.129229_b0295) 2018; 42 Phinzi (10.1016/j.jhydrol.2023.129229_b0335) 2021; 103 Kinnell (10.1016/j.jhydrol.2023.129229_b0225) 2010; 385 Emadodin (10.1016/j.jhydrol.2023.129229_b0155) 2012; 7 Aslam (10.1016/j.jhydrol.2023.129229_b0065) 2021; 12 Marques (10.1016/j.jhydrol.2023.129229_b0265) 1992; 19 Keshkamat (10.1016/j.jhydrol.2023.129229_b0205) 2013; 114 Tien Bui (10.1016/j.jhydrol.2023.129229_b0410) 2016; 540 Çimen (10.1016/j.jhydrol.2023.129229_b0100) 2008; 53 Thi Ngo (10.1016/j.jhydrol.2023.129229_b0400) 2021; 12 Abolhasani (10.1016/j.jhydrol.2023.129229_b0005) 2022; 33 10.1016/j.jhydrol.2023.129229_b0035 Kisi (10.1016/j.jhydrol.2023.129229_b0235) 2012; 450–451 Angileri (10.1016/j.jhydrol.2023.129229_b0055) 2016; 262 Mirzabaev (10.1016/j.jhydrol.2023.129229_b0275) 2016; 8 Dodangeh (10.1016/j.jhydrol.2023.129229_b0130) 2020; 590 Cooper (10.1016/j.jhydrol.2023.129229_b0110) 2012; 117 Pally (10.1016/j.jhydrol.2023.129229_b0315) 2022; 148 Yamashita (10.1016/j.jhydrol.2023.129229_b0475) 2018; 9 Sternberg (10.1016/j.jhydrol.2023.129229_b0385) 2000; 37 Vu (10.1016/j.jhydrol.2023.129229_b0435) 2020; 164 Abrahart (10.1016/j.jhydrol.2023.129229_b0010) 2012; 36 Tien Bui (10.1016/j.jhydrol.2023.129229_b0405) 2012; 2012 Vojinovic (10.1016/j.jhydrol.2023.129229_b091) 2013; 15 Trabelsi (10.1016/j.jhydrol.2023.129229_b0415) 2017; 112 Du (10.1016/j.jhydrol.2023.129229_b0140) 2022; 61 Kisi (10.1016/j.jhydrol.2023.129229_b0240) 2016; 122 Goyal (10.1016/j.jhydrol.2023.129229_b0200) 2014; 41 Sadeghi (10.1016/j.jhydrol.2023.129229_b0370) 2017; 63 Wainwright (10.1016/j.jhydrol.2023.129229_b0440) 2008; 33 Afshar (10.1016/j.jhydrol.2023.129229_b0020) 2010; 101 Amiri (10.1016/j.jhydrol.2023.129229_b0045) 2010; 3 10.1016/j.jhydrol.2023.129229_b0450 Capolongo (10.1016/j.jhydrol.2023.129229_b0080) 2008; 356 10.1016/j.jhydrol.2023.129229_b0455 Anastasakis (10.1016/j.jhydrol.2023.129229_b0050) 2001 Rosskopf (10.1016/j.jhydrol.2023.129229_b0365) 2020; 8 10.1016/j.jhydrol.2023.129229_b0460 Deng (10.1016/j.jhydrol.2023.129229_b0120) 2011; 16 10.1016/j.jhydrol.2023.129229_b0465 Chen (10.1016/j.jhydrol.2023.129229_b0090) 2006; 42 Donovan (10.1016/j.jhydrol.2023.129229_b0135) 2021; 287 Evans (10.1016/j.jhydrol.2023.129229_b0165) 1997; 17 Fang (10.1016/j.jhydrol.2023.129229_b0175) 2021; 594 Liu (10.1016/j.jhydrol.2023.129229_b0260) 2019; 163 Ebtehaj (10.1016/j.jhydrol.2023.129229_b0145) 2013; 7 Li (10.1016/j.jhydrol.2023.129229_b0255) 2021; 12 Tang (10.1016/j.jhydrol.2023.129229_b0395) 2015; 73 Auerswald (10.1016/j.jhydrol.2023.129229_b0070) 2006; 19 Raza (10.1016/j.jhydrol.2023.129229_b0350) 2021; 10 Robinson (10.1016/j.jhydrol.2023.129229_b0360) 1998 Melesse (10.1016/j.jhydrol.2023.129229_b0270) 2011; 98 Panagos (10.1016/j.jhydrol.2023.129229_b0320) 2015; 511 10.1016/j.jhydrol.2023.129229_b0470 Venkatappareddy (10.1016/j.jhydrol.2023.129229_b0430) 2021; 115 10.1016/j.jhydrol.2023.129229_b0355 Emadodin (10.1016/j.jhydrol.2023.129229_b0160) 2012; 14 Ajitha (10.1016/j.jhydrol.2023.129229_b0030) 2022; 212 Boudjemline (10.1016/j.jhydrol.2023.129229_b0075) 2018; 36 Yousefi (10.1016/j.jhydrol.2023.129229_b0490) 2021; 32 Cerdan (10.1016/j.jhydrol.2023.129229_b0085) 2010; 122 Panahi (10.1016/j.jhydrol.2023.129229_b0325) 2022; 1–23 Sajedi-Hosseini (10.1016/j.jhydrol.2023.129229_b0375) 2018; 29 Mutlu (10.1016/j.jhydrol.2023.129229_b0290) 2019; 8 Dibike (10.1016/j.jhydrol.2023.129229_b0125) 2001; 15 Ghorbanzadeh (10.1016/j.jhydrol.2023.129229_b0190) 2019; 11 Solaimani (10.1016/j.jhydrol.2023.129229_b0380) 2008; 11 Wijitkosum (10.1016/j.jhydrol.2023.129229_b0445) 2021; 9 Ahmad (10.1016/j.jhydrol.2023.129229_b0025) 2018; 203 Orgill (10.1016/j.jhydrol.2023.129229_b0310) 2018; 29 Kinnell (10.1016/j.jhydrol.2023.129229_b0230) 2017; 596–597 10.1016/j.jhydrol.2023.129229_b0245 Petroselli (10.1016/j.jhydrol.2023.129229_b0330) 2021; 8 Rapp (10.1016/j.jhydrol.2023.129229_b0345) 1963 Khosravi (10.1016/j.jhydrol.2023.129229_b0215) 2018; 22 Akhavan (10.1016/j.jhydrol.2023.129229_b0040) 2010; 139 Nearing (10.1016/j.jhydrol.2023.129229_b0300) 1989; 32 Khosravi (10.1016/j.jhydrol.2023.129229_b0220) 2020; 591 Yu (10.1016/j.jhydrol.2023.129229_b0495) 1996; 39 Novakovic (10.1016/j.jhydrol.2023.129229_b0305) 2009 Abuzaid (10.1016/j.jhydrol.2023.129229_b0015) 2021; 11 Gliessman (10.1016/j.jhydrol.2023.129229_b0195) 2004; 11 Mohammadi (10.1016/j.jhydrol.2023.129229_b0280) 2021; 14 Darvishan (10.1016/j.jhydrol.2023.129229_b0115) 2010; 42 Zhang (10.1016/j.jhydrol.2023.129229_b0500) 1996; 39 Chung (10.1016/j.jhydrol.2023.129229_b0095) 2003; 30 Ganguli (10.1016/j.jhydrol.2023.129229_b0185) 2014; 28 Yousefi (10.1016/j.jhydrol.2023.129229_b0485) 2016; 284 Khalili Moghadam (10.1016/j.jhydrol.2023.129229_b0210) 2015; 241–242 Mosavi (10.1016/j.jhydrol.2023.129229_b0285) 2020; 12 Tan (10.1016/j.jhydrol.2023.129229_b0390) 2018; 10 Yesilnacar (10.1016/j.jhydrol.2023.129229_b0480) 2005 Ebtehaj (10.1016/j.jhydrol.2023.129229_b0150) 2014; 28
References_xml	– volume: 98 start-page: 855 year: 2011 end-page: 866 ident: b0270 article-title: Suspended sediment load prediction of river systems: An artificial neural network approach publication-title: Agric. Water Manag. – volume: 1–23 year: 2022 ident: b0325 article-title: A country-wide assessment of Iran’s land subsidence susceptibility using satellite-based InSAR and machine learning publication-title: Geocarto Int. – volume: 11 start-page: 1426 year: 2021 ident: b0015 article-title: Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques publication-title: Agronomy – volume: 163 start-page: 332 year: 2019 end-page: 341 ident: b0260 article-title: CNN and RNN based payload classification methods for attack detection publication-title: Knowl.-Based Syst. – volume: 48 start-page: 39 year: 1999 end-page: 70 ident: b0340 article-title: Scales and processes of water and sediment redistribution in drylands: results from the Rambla Honda field site in Southeast Spain publication-title: Earth-Sci. Rev. – volume: 16 start-page: 751 year: 2011 end-page: 773 ident: b0120 article-title: Do roads lead to grassland degradation or restoration? A case study in Inner Mongolia, China publication-title: Environ. Dev. Econ. – volume: 7 start-page: 382 year: 2013 end-page: 392 ident: b0145 article-title: Evaluation of Sediment Transport in Sewer using Artificial Neural Network publication-title: Eng. Appl. Comput. Fluid Mech. – reference: Woolhiser, D.A., Smith, R.E., Goodrich, D.C., 1990. KINEROS:a kinematic runoff and erosion model: documentation and user manual. – volume: 15 start-page: 208 year: 2001 end-page: 216 ident: b0125 article-title: Model Induction with Support Vector Machines: Introduction and Applications publication-title: J. Comput. Civ. Eng. – volume: 450–451 start-page: 48 year: 2012 end-page: 58 ident: b0235 article-title: Suspended sediment modeling using genetic programming and soft computing techniques publication-title: J. Hydrol. – reference: Akbari, A.-M., 2017. Soil erosion in Iran 2.5 times the world average [WWW Document]. Tehran Times. URL https://www.tehrantimes.com/news/418381/Soil-erosion-in-Iran-2-5-times-the-world-average#:∼:text=TEHRAN — Iran has a mean, deputy agriculture minister has said. – volume: 9 start-page: 611 year: 2018 end-page: 629 ident: b0475 article-title: Convolutional neural networks: an overview and application in radiology publication-title: Insights Imaging – volume: 101 start-page: 606 year: 2010 end-page: 614 ident: b0020 article-title: Soil redistribution rate and its relationship with soil organic carbon and total nitrogen using 137Cs technique in a cultivated complex hillslope in western Iran publication-title: J. Environ. Radioact. – volume: 73 start-page: 1715 year: 2015 end-page: 1724 ident: b0395 article-title: Assessment of soil erosion using RUSLE and GIS: a case study of the Yangou watershed in the Loess Plateau, China publication-title: Environ. Earth Sci. – volume: 61 start-page: 9901 year: 2022 end-page: 9909 ident: b0140 article-title: Random noise attenuation via convolutional neural network in seismic datasets publication-title: Alexandria Eng. J. – volume: 142 start-page: 221 year: 2016 end-page: 232 ident: b0420 article-title: Developing an erodibility triangle for soil textures in semi-arid regions, NW Iran publication-title: CATENA – volume: 32 start-page: 1452 year: 2021 end-page: 1466 ident: b0490 article-title: Assessment of land degradation using machine-learning techniques: A case of declining rangelands publication-title: L. Degrad. Dev. – reference: Wischmeier, W.H., Smith, D.D., 1978. Predicting rainfall erosion losses: a guide to conservation planning. Science and Education Administration, U.S. Dept. of Agriculture. – volume: 30 start-page: 451 year: 2003 end-page: 472 ident: b0095 article-title: Validation of Spatial Prediction Models for Landslide Hazard Mapping publication-title: Nat. Hazards – volume: 103 start-page: 139 year: 2021 end-page: 162 ident: b0335 article-title: Soil erosion risk assessment in the Umzintlava catchment (T32E), Eastern Cape, South Africa, using RUSLE and random forest algorithm publication-title: South Afr. Geogr. J. – reference: Wischmeier, W.H., Smith, D.D., 1965. Predicting rainfall-erosion losses from cropland east of the Rocky Mountains:guide for selection of practices for soil and water conservation. Agricultural Research Service, U. S. Dept of Agriculture in cooperation with Purdue Agricultural Experiment Station. – volume: 12 start-page: 505 year: 2021 end-page: 519 ident: b0400 article-title: Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran publication-title: Geosci. Front. – volume: 287 year: 2021 ident: b0135 article-title: Impacts of grazing on ground cover, soil physical properties and soil loss via surface erosion: A novel geospatial modelling approach publication-title: J. Environ. Manage. – year: 1963 ident: b0345 article-title: Error assessment of the revised universal soil loss equation using natural runoff plot data – volume: 596–597 start-page: 32 year: 2017 end-page: 42 ident: b0230 article-title: A comparison of the abilities of the USLE-M, RUSLE2 and WEPP to model event erosion from bare fallow areas publication-title: Sci. Total Environ. – volume: 36 start-page: 17 year: 2018 end-page: 26 ident: b0075 article-title: Assessment and mapping of desertification sensitivity with MEDALUS model and GIS – Case study: basin of Hodna, Algeria publication-title: J. Water L. Dev. – volume: 15 start-page: 1408 year: 2013 end-page: 1424 ident: b091 article-title: A machine learning approach for estimation of shallow water depths from optical satellite images and sonar measurements publication-title: Journal of Hydroinformatics – volume: 42 start-page: 13 year: 2018 ident: b0295 article-title: Livestock grazing and trampling effects on plant functional composition at three wells in the desert steppe of Mongolia publication-title: J. Ecol. Environ. – volume: 10 start-page: 422 year: 2021 ident: b0350 article-title: Modeling Approaches to Assess Soil Erosion by Water at the Field Scale with Special Emphasis on Heterogeneity of Soils and Crops publication-title: Land – volume: 8 start-page: 171 year: 2021 ident: b0330 article-title: Comparative Evaluation of the Rainfall Erosivity in the Rieti Province, Central Italy, Using Empirical Formulas and a Stochastic Rainfall Generator publication-title: Hydrology – volume: 46 start-page: 287 year: 2008 end-page: 305 ident: b0105 article-title: Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: a test in Southern Sicily publication-title: Nat. Hazards – volume: 19 start-page: 305 year: 2006 end-page: 311 ident: b0070 article-title: Soil erosion potential of organic versus conventional farming evaluated by USLE modelling of cropping statistics for agricultural districts in Bavaria publication-title: Soil Use Manag. – volume: 385 start-page: 384 year: 2010 end-page: 397 ident: b0225 article-title: Event soil loss, runoff and the Universal Soil Loss Equation family of models: A review publication-title: J. Hydrol. – volume: 2012 start-page: 1 year: 2012 end-page: 26 ident: b0405 article-title: Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models publication-title: Math. Probl. Eng. – year: 2005 ident: b0480 article-title: The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey – volume: 36 start-page: 480 year: 2012 end-page: 513 ident: b0010 article-title: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting publication-title: Prog. Phys. Geogr. Earth Environ. – volume: 9 start-page: 217 year: 2021 end-page: 228 ident: b0445 article-title: Factor influencing land degradation sensitivity and desertification in a drought prone watershed in Thailand publication-title: Int. Soil Water Conserv. Res. – reference: Renard, K.G., Foster, G.R., Weesies, G.A., Mccool, D.K., Yoder, D.C., 1997. Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). – volume: 39 start-page: 559 year: 1996 end-page: 561 ident: b0495 article-title: Technical Notes: A Robust Estimator of the R-factor for the Universal Soil Loss Equation publication-title: Trans. ASAE – year: 1998 ident: b0360 article-title: Multi-objective optimisation of polynomial models for time series prediction using genetic algorithms and neural networks – volume: 33 start-page: 3358 year: 2022 end-page: 3374 ident: b0005 article-title: A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area publication-title: L. Degrad. Dev. – volume: 39 start-page: 855 year: 1996 end-page: 863 ident: b0500 article-title: Evaluation of WEPP Runoff And Soil Loss Predictions Using Natural Runoff Plot Data publication-title: Trans. ASAE – volume: 53 start-page: 656 year: 2008 end-page: 666 ident: b0100 article-title: Estimation of daily suspended sediments using support vector machines publication-title: Hydrol. Sci. J. – reference: Williams, J.R., 1975. Sediment-yield prediction with Universal Equation using runoff energy factor. – volume: 12 start-page: 3089 year: 2021 end-page: 3113 ident: b0255 article-title: Temporal detection of sharp landslide deformation with ensemble-based LSTM-RNNs and Hurst exponent. Geomatics publication-title: Nat. Hazards Risk – volume: 262 start-page: 61 year: 2016 end-page: 76 ident: b0055 article-title: Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy) publication-title: Geomorphology – volume: 8 start-page: 578 year: 2019 ident: b0290 article-title: An experimental research on the use of recurrent neural networks in landslide susceptibility mapping ISPRS publication-title: Int. J. Geo-Inf. – volume: 14 start-page: 611 year: 2012 end-page: 625 ident: b0160 article-title: Soil degradation and agricultural sustainability: an overview from Iran publication-title: Environ. Dev. Sustain. – volume: 22 start-page: 4771 year: 2018 end-page: 4792 ident: b0215 article-title: Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization publication-title: Hydrol. Earth Syst. Sci. – volume: 42 start-page: 51 year: 2010 end-page: 60 ident: b0115 article-title: Efficacy of Time-Area Method in simulating temporal variation of sediment yield in Chehelgazi watershed, Iran. Ann. Warsaw Univ publication-title: Life Sci. - SGGW. L. Reclam. – volume: 63 year: 2017 ident: b0370 article-title: Soil erosion in Iran: State of the art, tendency and solutions publication-title: J. Agric. For. – volume: 203 start-page: 810 year: 2018 end-page: 821 ident: b0025 article-title: Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees publication-title: J. Clean. Prod. – volume: 112 start-page: 186 year: 2017 end-page: 194 ident: b0415 article-title: A New Feature Selection Method for Nominal Classifier based on Formal Concept Analysis publication-title: Proc. Comput. Sci. – volume: 540 start-page: 317 year: 2016 end-page: 330 ident: b0410 article-title: Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS publication-title: J. Hydrol. – volume: 164 year: 2020 ident: b0435 article-title: Machine learning based soil erosion susceptibility prediction using social spider algorithm optimized multivariate adaptive regression spline publication-title: Measurement – start-page: 1351 year: 2009 end-page: 1354 ident: b0305 article-title: Using Information Gain Attribute Evaluation to Classify Sonar Targets publication-title: 17th Telecommunications Forum TELFOR 2009 – volume: 41 start-page: 5267 year: 2014 end-page: 5276 ident: b0200 article-title: Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS publication-title: Expert Syst. Appl. – volume: 14 start-page: 1019 year: 2021 end-page: 1039 ident: b0280 article-title: Country-scale spatio-temporal monitoring of soil erosion in Iran using the G2 model publication-title: Int. J. Digit. Earth – volume: 29 start-page: 274 year: 2018 end-page: 283 ident: b0310 article-title: Removing Grazing Pressure from a Native Pasture Decreases Soil Organic Carbon in Southern New South Wales, Australia publication-title: L. Degrad. Dev. – volume: 511 start-page: 801 year: 2015 end-page: 814 ident: b0320 article-title: Rainfall erosivity in Europe publication-title: Sci. Total Environ. – volume: 29 start-page: 3092 year: 2018 end-page: 3103 ident: b0375 article-title: Spatial prediction of soil erosion susceptibility using a fuzzy analytical network process: Application of the fuzzy decision making trial and evaluation laboratory approach publication-title: L. Degrad. Dev. – volume: 212 year: 2022 ident: b0030 article-title: Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN publication-title: Electr. Power Syst. Res. – volume: 32 start-page: 1587 year: 1989 end-page: 1593 ident: b0300 article-title: A process-based soil erosion model for USDA-water erosion prediction project technology publication-title: Trans. ASAE – volume: 356 start-page: 119 year: 2008 end-page: 130 ident: b0080 article-title: Analyzing temporal changes in climate erosivity using a simplified rainfall erosivity model in Basilicata (southern Italy) publication-title: J. Hydrol. – volume: 114 start-page: 433 year: 2013 end-page: 444 ident: b0205 article-title: Understanding transportation-caused rangeland damage in Mongolia publication-title: J. Environ. Manage. – volume: 11 start-page: 61 year: 2004 end-page: 80 ident: b0195 article-title: Integrating Agroecological Processes into Cropping Systems Research publication-title: J. Crop Improv. – volume: 117 start-page: n/a year: 2012 end-page: n/a ident: b0110 article-title: A new approach for simulating the redistribution of soil particles by water erosion: A marker-in-cell model publication-title: J. Geophys. Res. – volume: 8 start-page: 354 year: 2020 end-page: 362 ident: b0365 article-title: Assessing spatial variability and erosion susceptibility of soils in hilly agricultural areas in Southern Italy publication-title: Int. Soil Water Conserv. Res. – volume: 33 start-page: 813 year: 2008 end-page: 826 ident: b0440 article-title: A transport-distance approach to scaling erosion rates: 1. Background and model development publication-title: Earth Surf. Process. Landforms – volume: 591 year: 2020 ident: b0220 article-title: Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran publication-title: J. Hydrol. – volume: 148 year: 2022 ident: b0315 article-title: Application of image processing and convolutional neural networks for flood image classification and semantic segmentation publication-title: Environ. Model. Softw. – volume: 10 start-page: 2192 year: 2018 end-page: 2213 ident: b0390 article-title: Modeling Sediment Yield in Land Surface and Earth System Models: Model Comparison, Development, and Evaluation publication-title: J. Adv. Model. Earth Syst. – volume: 74 start-page: 5677 year: 2015 end-page: 5685 ident: b0170 article-title: Aspect-induced differences in soil erosion intensity in a gullied hilly region on the Chinese Loess Plateau publication-title: Environ. Earth Sci. – volume: 17 start-page: 127 year: 1997 end-page: 141 ident: b0165 article-title: Soil erosion in the UK initiated by grazing animals publication-title: Appl. Geogr. – volume: 122 start-page: 112 year: 2016 end-page: 117 ident: b0240 article-title: Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree publication-title: Comput. Electron. Agric. – volume: 594 year: 2021 ident: b0175 article-title: Predicting flood susceptibility using LSTM neural networks publication-title: J. Hydrol. – volume: 11 start-page: 1893 year: 2008 end-page: 1900 ident: b0380 article-title: Application of IRS-1D Data in Water Erosion Features Detection (Case Study: Nour Roud Catchment, Iran) publication-title: Pakistan J. Biol. Sci. – volume: 12 start-page: 1995 year: 2020 ident: b0285 article-title: Susceptibility Mapping of Soil Water Erosion Using Machine Learning Models publication-title: Water – volume: 11 start-page: 2046 year: 2019 ident: b0190 article-title: UAV-based slope failure detection using deep-learning convolutional neural networks publication-title: Remote Sens. – volume: 37 start-page: 224 year: 2000 end-page: 237 ident: b0385 article-title: Vegetation response to grazing management in a Mediterranean herbaceous community: a functional group approach publication-title: J. Appl. Ecol. – volume: 284 start-page: 103 year: 2016 end-page: 112 ident: b0485 article-title: Effects of road construction on soil degradation and nutrient transport in Caspian Hyrcanian mixed forests publication-title: Geoderma – year: 2001 ident: b0050 article-title: The Development of Self-Organization Techniques in Modelling: A Review of the Group Method of Data Handling (GMDH) publication-title: United Kingdom – volume: 241–242 start-page: 210 year: 2015 end-page: 220 ident: b0210 article-title: Effects of land use change on soil splash erosion in the semi-arid region of Iran publication-title: Geoderma – volume: 122 start-page: 167 year: 2010 end-page: 177 ident: b0085 article-title: Rates and spatial variations of soil erosion in Europe: A study based on erosion plot data publication-title: Geomorphology – volume: 12 start-page: 1637 year: 2021 end-page: 1649 ident: b0065 article-title: Soil erosion susceptibility mapping using a GIS-based multi-criteria decision approach: Case of district Chitral, Pakistan publication-title: Ain Shams Eng. J. – volume: 28 start-page: 4989 year: 2014 end-page: 5009 ident: b0185 article-title: Ensemble prediction of regional droughts using climate inputs and the SVM-copula approach publication-title: Hydrol. Process. – volume: 8 start-page: 93 year: 2016 end-page: 108 ident: b0275 article-title: Rangelands of Central Asia: challenges and opportunities publication-title: J. Arid Land – volume: 42 year: 2006 ident: b0090 article-title: Green-Ampt infiltration model for sloping surfaces publication-title: Water Resour. Res. – volume: 139 start-page: 675 year: 2010 end-page: 688 ident: b0040 article-title: Application of SWAT model to investigate nitrate leaching in Hamadan-Bahar Watershed publication-title: Iran. Agric. Ecosyst. Environ. – reference: . – volume: 3 start-page: ASWR.S3427 year: 2010 ident: b0045 article-title: Estimate of Erosion and Sedimentation in Semi-arid Basin using Empirical Models of Erosion Potential within a Geographic Information System. Air publication-title: Soil Water Res. – volume: 28 start-page: 4765 year: 2014 end-page: 4779 ident: b0150 article-title: Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers publication-title: Water Resour. Manag. – volume: 7 start-page: 203 year: 2012 end-page: 219 ident: b0155 article-title: Degradation of soils as a result of long-term human-induced transformation of the environment in Iran: an overview publication-title: J. Land Use Sci. – reference: World Bank, 2005. Islamic Republic of Iran, Cost Assessment of Environmental Degradation. – volume: 19 start-page: 333 year: 1992 end-page: 344 ident: b0265 article-title: The influence of aspect on runoff and soil loss in a Mediterranean burnt forest (Spain) publication-title: CATENA – reference: Laylin, D., 2018. Environmental and wildlife degradation in Iran [WWW Document]. Atl. Counc. URL – volume: 590 year: 2020 ident: b0130 article-title: Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search publication-title: J. Hydrol. – volume: 115 year: 2021 ident: b0430 article-title: A Legendre polynomial based activation function: An aid for modeling of max pooling publication-title: Digit. Signal Process. – volume: 2012 start-page: 1 year: 2012 ident: 10.1016/j.jhydrol.2023.129229_b0405 article-title: Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models publication-title: Math. Probl. Eng. doi: 10.1155/2012/974638 – year: 1963 ident: 10.1016/j.jhydrol.2023.129229_b0345 – volume: 42 issue: 7 year: 2006 ident: 10.1016/j.jhydrol.2023.129229_b0090 article-title: Green-Ampt infiltration model for sloping surfaces publication-title: Water Resour. Res. doi: 10.1029/2005WR004468 – volume: 596–597 start-page: 32 year: 2017 ident: 10.1016/j.jhydrol.2023.129229_b0230 article-title: A comparison of the abilities of the USLE-M, RUSLE2 and WEPP to model event erosion from bare fallow areas publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2017.04.046 – volume: 11 start-page: 61 year: 2004 ident: 10.1016/j.jhydrol.2023.129229_b0195 article-title: Integrating Agroecological Processes into Cropping Systems Research publication-title: J. Crop Improv. doi: 10.1300/J411v11n01_04 – volume: 103 start-page: 139 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0335 article-title: Soil erosion risk assessment in the Umzintlava catchment (T32E), Eastern Cape, South Africa, using RUSLE and random forest algorithm publication-title: South Afr. Geogr. J. doi: 10.1080/03736245.2020.1716838 – volume: 74 start-page: 5677 year: 2015 ident: 10.1016/j.jhydrol.2023.129229_b0170 article-title: Aspect-induced differences in soil erosion intensity in a gullied hilly region on the Chinese Loess Plateau publication-title: Environ. Earth Sci. doi: 10.1007/s12665-015-4648-4 – year: 2001 ident: 10.1016/j.jhydrol.2023.129229_b0050 article-title: The Development of Self-Organization Techniques in Modelling: A Review of the Group Method of Data Handling (GMDH) publication-title: United Kingdom – volume: 8 start-page: 578 issue: 12 year: 2019 ident: 10.1016/j.jhydrol.2023.129229_b0290 article-title: An experimental research on the use of recurrent neural networks in landslide susceptibility mapping ISPRS publication-title: Int. J. Geo-Inf. doi: 10.3390/ijgi8120578 – volume: 28 start-page: 4989 year: 2014 ident: 10.1016/j.jhydrol.2023.129229_b0185 article-title: Ensemble prediction of regional droughts using climate inputs and the SVM-copula approach publication-title: Hydrol. Process. doi: 10.1002/hyp.9966 – volume: 98 start-page: 855 year: 2011 ident: 10.1016/j.jhydrol.2023.129229_b0270 article-title: Suspended sediment load prediction of river systems: An artificial neural network approach publication-title: Agric. Water Manag. doi: 10.1016/j.agwat.2010.12.012 – volume: 48 start-page: 39 year: 1999 ident: 10.1016/j.jhydrol.2023.129229_b0340 article-title: Scales and processes of water and sediment redistribution in drylands: results from the Rambla Honda field site in Southeast Spain publication-title: Earth-Sci. Rev. doi: 10.1016/S0012-8252(99)00046-X – volume: 73 start-page: 1715 year: 2015 ident: 10.1016/j.jhydrol.2023.129229_b0395 article-title: Assessment of soil erosion using RUSLE and GIS: a case study of the Yangou watershed in the Loess Plateau, China publication-title: Environ. Earth Sci. doi: 10.1007/s12665-014-3523-z – volume: 139 start-page: 675 year: 2010 ident: 10.1016/j.jhydrol.2023.129229_b0040 article-title: Application of SWAT model to investigate nitrate leaching in Hamadan-Bahar Watershed publication-title: Iran. Agric. Ecosyst. Environ. doi: 10.1016/j.agee.2010.10.015 – volume: 122 start-page: 112 year: 2016 ident: 10.1016/j.jhydrol.2023.129229_b0240 article-title: Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2016.01.026 – volume: 42 start-page: 13 year: 2018 ident: 10.1016/j.jhydrol.2023.129229_b0295 article-title: Livestock grazing and trampling effects on plant functional composition at three wells in the desert steppe of Mongolia publication-title: J. Ecol. Environ. doi: 10.1186/s41610-018-0075-2 – volume: 32 start-page: 1452 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0490 article-title: Assessment of land degradation using machine-learning techniques: A case of declining rangelands publication-title: L. Degrad. Dev. doi: 10.1002/ldr.3794 – ident: 10.1016/j.jhydrol.2023.129229_b0035 – volume: 15 start-page: 1408 issue: 4 year: 2013 ident: 10.1016/j.jhydrol.2023.129229_b091 article-title: A machine learning approach for estimation of shallow water depths from optical satellite images and sonar measurements publication-title: Journal of Hydroinformatics doi: 10.2166/hydro.2013.234 – volume: 29 start-page: 274 year: 2018 ident: 10.1016/j.jhydrol.2023.129229_b0310 article-title: Removing Grazing Pressure from a Native Pasture Decreases Soil Organic Carbon in Southern New South Wales, Australia publication-title: L. Degrad. Dev. doi: 10.1002/ldr.2560 – volume: 148 year: 2022 ident: 10.1016/j.jhydrol.2023.129229_b0315 article-title: Application of image processing and convolutional neural networks for flood image classification and semantic segmentation publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2021.105285 – volume: 287 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0135 article-title: Impacts of grazing on ground cover, soil physical properties and soil loss via surface erosion: A novel geospatial modelling approach publication-title: J. Environ. Manage. doi: 10.1016/j.jenvman.2021.112206 – volume: 8 start-page: 171 issue: 4 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0330 article-title: Comparative Evaluation of the Rainfall Erosivity in the Rieti Province, Central Italy, Using Empirical Formulas and a Stochastic Rainfall Generator publication-title: Hydrology doi: 10.3390/hydrology8040171 – volume: 8 start-page: 354 year: 2020 ident: 10.1016/j.jhydrol.2023.129229_b0365 article-title: Assessing spatial variability and erosion susceptibility of soils in hilly agricultural areas in Southern Italy publication-title: Int. Soil Water Conserv. Res. doi: 10.1016/j.iswcr.2020.09.005 – ident: 10.1016/j.jhydrol.2023.129229_b0465 – volume: 9 start-page: 217 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0445 article-title: Factor influencing land degradation sensitivity and desertification in a drought prone watershed in Thailand publication-title: Int. Soil Water Conserv. Res. doi: 10.1016/j.iswcr.2020.10.005 – volume: 540 start-page: 317 year: 2016 ident: 10.1016/j.jhydrol.2023.129229_b0410 article-title: Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.06.027 – start-page: 1351 year: 2009 ident: 10.1016/j.jhydrol.2023.129229_b0305 article-title: Using Information Gain Attribute Evaluation to Classify Sonar Targets – volume: 385 start-page: 384 year: 2010 ident: 10.1016/j.jhydrol.2023.129229_b0225 article-title: Event soil loss, runoff and the Universal Soil Loss Equation family of models: A review publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2010.01.024 – volume: 46 start-page: 287 year: 2008 ident: 10.1016/j.jhydrol.2023.129229_b0105 article-title: Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: a test in Southern Sicily publication-title: Nat. Hazards doi: 10.1007/s11069-007-9188-0 – year: 2005 ident: 10.1016/j.jhydrol.2023.129229_b0480 – ident: 10.1016/j.jhydrol.2023.129229_b0355 – volume: 28 start-page: 4765 year: 2014 ident: 10.1016/j.jhydrol.2023.129229_b0150 article-title: Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers publication-title: Water Resour. Manag. doi: 10.1007/s11269-014-0774-0 – ident: 10.1016/j.jhydrol.2023.129229_b0460 – volume: 11 start-page: 1426 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0015 article-title: Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques publication-title: Agronomy doi: 10.3390/agronomy11071426 – volume: 203 start-page: 810 year: 2018 ident: 10.1016/j.jhydrol.2023.129229_b0025 article-title: Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2018.08.207 – volume: 7 start-page: 382 year: 2013 ident: 10.1016/j.jhydrol.2023.129229_b0145 article-title: Evaluation of Sediment Transport in Sewer using Artificial Neural Network publication-title: Eng. Appl. Comput. Fluid Mech. – volume: 1–23 year: 2022 ident: 10.1016/j.jhydrol.2023.129229_b0325 article-title: A country-wide assessment of Iran’s land subsidence susceptibility using satellite-based InSAR and machine learning publication-title: Geocarto Int. – volume: 36 start-page: 17 year: 2018 ident: 10.1016/j.jhydrol.2023.129229_b0075 article-title: Assessment and mapping of desertification sensitivity with MEDALUS model and GIS – Case study: basin of Hodna, Algeria publication-title: J. Water L. Dev. doi: 10.2478/jwld-2018-0002 – volume: 115 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0430 article-title: A Legendre polynomial based activation function: An aid for modeling of max pooling publication-title: Digit. Signal Process. doi: 10.1016/j.dsp.2021.103093 – volume: 14 start-page: 611 year: 2012 ident: 10.1016/j.jhydrol.2023.129229_b0160 article-title: Soil degradation and agricultural sustainability: an overview from Iran publication-title: Environ. Dev. Sustain. doi: 10.1007/s10668-012-9351-y – volume: 41 start-page: 5267 year: 2014 ident: 10.1016/j.jhydrol.2023.129229_b0200 article-title: Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.02.047 – ident: 10.1016/j.jhydrol.2023.129229_b0245 – volume: 284 start-page: 103 year: 2016 ident: 10.1016/j.jhydrol.2023.129229_b0485 article-title: Effects of road construction on soil degradation and nutrient transport in Caspian Hyrcanian mixed forests publication-title: Geoderma doi: 10.1016/j.geoderma.2016.09.002 – volume: 53 start-page: 656 year: 2008 ident: 10.1016/j.jhydrol.2023.129229_b0100 article-title: Estimation of daily suspended sediments using support vector machines publication-title: Hydrol. Sci. J. doi: 10.1623/hysj.53.3.656 – volume: 7 start-page: 203 year: 2012 ident: 10.1016/j.jhydrol.2023.129229_b0155 article-title: Degradation of soils as a result of long-term human-induced transformation of the environment in Iran: an overview publication-title: J. Land Use Sci. doi: 10.1080/1747423X.2011.560292 – volume: 14 start-page: 1019 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0280 article-title: Country-scale spatio-temporal monitoring of soil erosion in Iran using the G2 model publication-title: Int. J. Digit. Earth doi: 10.1080/17538947.2021.1919230 – volume: 42 start-page: 51 year: 2010 ident: 10.1016/j.jhydrol.2023.129229_b0115 article-title: Efficacy of Time-Area Method in simulating temporal variation of sediment yield in Chehelgazi watershed, Iran. Ann. Warsaw Univ publication-title: Life Sci. - SGGW. L. Reclam. – volume: 8 start-page: 93 year: 2016 ident: 10.1016/j.jhydrol.2023.129229_b0275 article-title: Rangelands of Central Asia: challenges and opportunities publication-title: J. Arid Land doi: 10.1007/s40333-015-0057-5 – volume: 511 start-page: 801 year: 2015 ident: 10.1016/j.jhydrol.2023.129229_b0320 article-title: Rainfall erosivity in Europe publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2015.01.008 – volume: 15 start-page: 208 year: 2001 ident: 10.1016/j.jhydrol.2023.129229_b0125 article-title: Model Induction with Support Vector Machines: Introduction and Applications publication-title: J. Comput. Civ. Eng. doi: 10.1061/(ASCE)0887-3801(2001)15:3(208) – volume: 594 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0175 article-title: Predicting flood susceptibility using LSTM neural networks publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2020.125734 – volume: 101 start-page: 606 year: 2010 ident: 10.1016/j.jhydrol.2023.129229_b0020 article-title: Soil redistribution rate and its relationship with soil organic carbon and total nitrogen using 137Cs technique in a cultivated complex hillslope in western Iran publication-title: J. Environ. Radioact. doi: 10.1016/j.jenvrad.2010.03.008 – volume: 32 start-page: 1587 year: 1989 ident: 10.1016/j.jhydrol.2023.129229_b0300 article-title: A process-based soil erosion model for USDA-water erosion prediction project technology publication-title: Trans. ASAE doi: 10.13031/2013.31195 – volume: 17 start-page: 127 year: 1997 ident: 10.1016/j.jhydrol.2023.129229_b0165 article-title: Soil erosion in the UK initiated by grazing animals publication-title: Appl. Geogr. doi: 10.1016/S0143-6228(97)00002-7 – volume: 117 start-page: n/a issue: F4 year: 2012 ident: 10.1016/j.jhydrol.2023.129229_b0110 article-title: A new approach for simulating the redistribution of soil particles by water erosion: A marker-in-cell model publication-title: J. Geophys. Res. – volume: 122 start-page: 167 year: 2010 ident: 10.1016/j.jhydrol.2023.129229_b0085 article-title: Rates and spatial variations of soil erosion in Europe: A study based on erosion plot data publication-title: Geomorphology doi: 10.1016/j.geomorph.2010.06.011 – volume: 39 start-page: 855 year: 1996 ident: 10.1016/j.jhydrol.2023.129229_b0500 article-title: Evaluation of WEPP Runoff And Soil Loss Predictions Using Natural Runoff Plot Data publication-title: Trans. ASAE doi: 10.13031/2013.27570 – volume: 241–242 start-page: 210 year: 2015 ident: 10.1016/j.jhydrol.2023.129229_b0210 article-title: Effects of land use change on soil splash erosion in the semi-arid region of Iran publication-title: Geoderma doi: 10.1016/j.geoderma.2014.11.025 – volume: 39 start-page: 559 year: 1996 ident: 10.1016/j.jhydrol.2023.129229_b0495 article-title: Technical Notes: A Robust Estimator of the R-factor for the Universal Soil Loss Equation publication-title: Trans. ASAE doi: 10.13031/2013.27535 – volume: 164 year: 2020 ident: 10.1016/j.jhydrol.2023.129229_b0435 article-title: Machine learning based soil erosion susceptibility prediction using social spider algorithm optimized multivariate adaptive regression spline publication-title: Measurement doi: 10.1016/j.measurement.2020.108066 – volume: 114 start-page: 433 year: 2013 ident: 10.1016/j.jhydrol.2023.129229_b0205 article-title: Understanding transportation-caused rangeland damage in Mongolia publication-title: J. Environ. Manage. doi: 10.1016/j.jenvman.2012.10.043 – volume: 142 start-page: 221 year: 2016 ident: 10.1016/j.jhydrol.2023.129229_b0420 article-title: Developing an erodibility triangle for soil textures in semi-arid regions, NW Iran publication-title: CATENA doi: 10.1016/j.catena.2016.03.015 – ident: 10.1016/j.jhydrol.2023.129229_b0455 – volume: 262 start-page: 61 year: 2016 ident: 10.1016/j.jhydrol.2023.129229_b0055 article-title: Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy) publication-title: Geomorphology doi: 10.1016/j.geomorph.2016.03.018 – volume: 22 start-page: 4771 year: 2018 ident: 10.1016/j.jhydrol.2023.129229_b0215 article-title: Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-22-4771-2018 – volume: 591 year: 2020 ident: 10.1016/j.jhydrol.2023.129229_b0220 article-title: Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2020.125552 – volume: 29 start-page: 3092 year: 2018 ident: 10.1016/j.jhydrol.2023.129229_b0375 article-title: Spatial prediction of soil erosion susceptibility using a fuzzy analytical network process: Application of the fuzzy decision making trial and evaluation laboratory approach publication-title: L. Degrad. Dev. doi: 10.1002/ldr.3058 – volume: 11 start-page: 2046 year: 2019 ident: 10.1016/j.jhydrol.2023.129229_b0190 article-title: UAV-based slope failure detection using deep-learning convolutional neural networks publication-title: Remote Sens. doi: 10.3390/rs11172046 – volume: 356 start-page: 119 issue: 1–2 year: 2008 ident: 10.1016/j.jhydrol.2023.129229_b0080 article-title: Analyzing temporal changes in climate erosivity using a simplified rainfall erosivity model in Basilicata (southern Italy) publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2008.04.002 – volume: 450–451 start-page: 48 year: 2012 ident: 10.1016/j.jhydrol.2023.129229_b0235 article-title: Suspended sediment modeling using genetic programming and soft computing techniques publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2012.05.031 – volume: 33 start-page: 3358 issue: 17 year: 2022 ident: 10.1016/j.jhydrol.2023.129229_b0005 article-title: A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area publication-title: L. Degrad. Dev. doi: 10.1002/ldr.4391 – volume: 19 start-page: 305 year: 2006 ident: 10.1016/j.jhydrol.2023.129229_b0070 article-title: Soil erosion potential of organic versus conventional farming evaluated by USLE modelling of cropping statistics for agricultural districts in Bavaria publication-title: Soil Use Manag. doi: 10.1111/j.1475-2743.2003.tb00320.x – volume: 10 start-page: 2192 year: 2018 ident: 10.1016/j.jhydrol.2023.129229_b0390 article-title: Modeling Sediment Yield in Land Surface and Earth System Models: Model Comparison, Development, and Evaluation publication-title: J. Adv. Model. Earth Syst. doi: 10.1029/2017MS001270 – volume: 30 start-page: 451 year: 2003 ident: 10.1016/j.jhydrol.2023.129229_b0095 article-title: Validation of Spatial Prediction Models for Landslide Hazard Mapping publication-title: Nat. Hazards doi: 10.1023/B:NHAZ.0000007172.62651.2b – volume: 112 start-page: 186 year: 2017 ident: 10.1016/j.jhydrol.2023.129229_b0415 article-title: A New Feature Selection Method for Nominal Classifier based on Formal Concept Analysis publication-title: Proc. Comput. Sci. doi: 10.1016/j.procs.2017.08.227 – volume: 36 start-page: 480 year: 2012 ident: 10.1016/j.jhydrol.2023.129229_b0010 article-title: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting publication-title: Prog. Phys. Geogr. Earth Environ. doi: 10.1177/0309133312444943 – ident: 10.1016/j.jhydrol.2023.129229_b0450 – volume: 33 start-page: 813 year: 2008 ident: 10.1016/j.jhydrol.2023.129229_b0440 article-title: A transport-distance approach to scaling erosion rates: 1. Background and model development publication-title: Earth Surf. Process. Landforms doi: 10.1002/esp.1624 – volume: 590 year: 2020 ident: 10.1016/j.jhydrol.2023.129229_b0130 article-title: Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2020.125423 – volume: 19 start-page: 333 issue: 3–4 year: 1992 ident: 10.1016/j.jhydrol.2023.129229_b0265 article-title: The influence of aspect on runoff and soil loss in a Mediterranean burnt forest (Spain) publication-title: CATENA doi: 10.1016/0341-8162(92)90007-X – volume: 3 start-page: ASWR.S3427 year: 2010 ident: 10.1016/j.jhydrol.2023.129229_b0045 article-title: Estimate of Erosion and Sedimentation in Semi-arid Basin using Empirical Models of Erosion Potential within a Geographic Information System. Air publication-title: Soil Water Res. doi: 10.4137/ASWR.S3427 – volume: 16 start-page: 751 year: 2011 ident: 10.1016/j.jhydrol.2023.129229_b0120 article-title: Do roads lead to grassland degradation or restoration? A case study in Inner Mongolia, China publication-title: Environ. Dev. Econ. doi: 10.1017/S1355770X11000180 – year: 1998 ident: 10.1016/j.jhydrol.2023.129229_b0360 – volume: 12 start-page: 1637 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0065 article-title: Soil erosion susceptibility mapping using a GIS-based multi-criteria decision approach: Case of district Chitral, Pakistan publication-title: Ain Shams Eng. J. doi: 10.1016/j.asej.2020.09.015 – volume: 212 year: 2022 ident: 10.1016/j.jhydrol.2023.129229_b0030 article-title: Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2022.108635 – volume: 12 start-page: 505 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0400 article-title: Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran publication-title: Geosci. Front. doi: 10.1016/j.gsf.2020.06.013 – volume: 9 start-page: 611 year: 2018 ident: 10.1016/j.jhydrol.2023.129229_b0475 article-title: Convolutional neural networks: an overview and application in radiology publication-title: Insights Imaging doi: 10.1007/s13244-018-0639-9 – ident: 10.1016/j.jhydrol.2023.129229_b0470 – volume: 12 start-page: 3089 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0255 article-title: Temporal detection of sharp landslide deformation with ensemble-based LSTM-RNNs and Hurst exponent. Geomatics publication-title: Nat. Hazards Risk doi: 10.1080/19475705.2021.1994474 – volume: 10 start-page: 422 year: 2021 ident: 10.1016/j.jhydrol.2023.129229_b0350 article-title: Modeling Approaches to Assess Soil Erosion by Water at the Field Scale with Special Emphasis on Heterogeneity of Soils and Crops publication-title: Land doi: 10.3390/land10040422 – volume: 12 start-page: 1995 year: 2020 ident: 10.1016/j.jhydrol.2023.129229_b0285 article-title: Susceptibility Mapping of Soil Water Erosion Using Machine Learning Models publication-title: Water doi: 10.3390/w12071995 – volume: 61 start-page: 9901 year: 2022 ident: 10.1016/j.jhydrol.2023.129229_b0140 article-title: Random noise attenuation via convolutional neural network in seismic datasets publication-title: Alexandria Eng. J. doi: 10.1016/j.aej.2022.03.008 – volume: 11 start-page: 1893 year: 2008 ident: 10.1016/j.jhydrol.2023.129229_b0380 article-title: Application of IRS-1D Data in Water Erosion Features Detection (Case Study: Nour Roud Catchment, Iran) publication-title: Pakistan J. Biol. Sci. doi: 10.3923/pjbs.2008.1893.1900 – volume: 37 start-page: 224 year: 2000 ident: 10.1016/j.jhydrol.2023.129229_b0385 article-title: Vegetation response to grazing management in a Mediterranean herbaceous community: a functional group approach publication-title: J. Appl. Ecol. doi: 10.1046/j.1365-2664.2000.00491.x – volume: 163 start-page: 332 year: 2019 ident: 10.1016/j.jhydrol.2023.129229_b0260 article-title: CNN and RNN based payload classification methods for attack detection publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2018.08.036 – volume: 63 year: 2017 ident: 10.1016/j.jhydrol.2023.129229_b0370 article-title: Soil erosion in Iran: State of the art, tendency and solutions publication-title: J. Agric. For.
SSID	ssj0000334
Score	2.6681688
Snippet	•Soil water erosion (SWE) is predicted though three kinds of deep learning algorithms.•Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and... Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land degradation and soil loss, and for mitigating the negative impacts...
SourceID	swepub proquest crossref elsevier
SourceType	Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	129229
SubjectTerms	CNN Deep learning ecosystems erosion control infrastructure Land degradation LSTM neural networks prediction RNN Soil erosion soil water water erosion water quality watersheds
Title	Soil water erosion susceptibility assessment using deep learning algorithms
URI	https://dx.doi.org/10.1016/j.jhydrol.2023.129229 https://www.proquest.com/docview/2834209445 https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-325309
Volume	618
WOSCitedRecordID	wos000942641000001&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-2707 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000334 issn: 1879-2707 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1ba9swFBZZO9hexq40u-HB2EuwF1uSLT-Grt0NyujCCHsRki3HyVI7OE7W7NfvyPIlJRvdBnsxxiDL0vf56Eg65xNCL0PBiAfjkO0llNpESvilKInsxBMswUIGrmcOmwjOzthkEn7q9bZNLsxmEWQZu7wMl_8VangGYOvU2b-Au30pPIB7AB2uADtc_wj4z_lsMfgutPihgiFQw7tar6rolSoQdjsQrRrnYF0tFcRKLZvzI6YDsZjmxaxMax3zfc813caFkW4C93R0oaUWYs2rLm47zVeF2JhAgVRI0cbWnKsfwuyInMIXXqh2Mfo4z5eGPVXc7uDcaV-mgy9LYTLiv4q0ELsrFR7uQrXM8tleCk2bTuD65qRjRxkrzIJQJ8oFu2baN3Z6z-Sb1Ye5MzeNd3TNDngxXt3qq2raenPa09XB1EtLBeEb6NALaAg2_XD0_mTyoRvGMSaN1Lwu0KV_vf5lZb9zbHYnLrtitJUDM76L7tT4WSPDmHuop7L76NZbVWuWP0AfNXOsijlWzRzrKnOsjjlWxRxLM8dqmGN1zHmIxqcn4-N3dn3Whi0IHZY2jYNE51QL4atQeS6OBIsC35VMQtuZxNKNAhwySYKhjJSPFZVUxDQaRkSKCD9CB1meqSNkscgnoQJPV-IE5v4ei2FG7ybgespQJW7cR6TpJx7VOvT6OJQFbwIO57zuXq67l5vu7SOnLbY0QizXFWANCLz2Jo2XyIE51xV90YDGwdrqLTSRqXy94uCMa9NGCO2jVwbN9mu0UPub2ZcRz4sp_1amHHsUD8PH__4dT9Dt7j96ig7KYq2eoZvRppytiuc1YX8C4Ym4sQ
linkProvider	Elsevier
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=Soil+water+erosion+susceptibility+assessment+using+deep+learning+algorithms&rft.jtitle=Journal+of+hydrology+%28Amsterdam%29&rft.au=Khosravi%2C+Khabat&rft.au=Rezaie%2C+Fatemeh&rft.au=Cooper%2C+James+R.&rft.au=Kalantari%2C+Zahra&rft.date=2023-03-01&rft.pub=Elsevier+B.V&rft.issn=0022-1694&rft.eissn=1879-2707&rft.volume=618&rft_id=info:doi/10.1016%2Fj.jhydrol.2023.129229&rft.externalDocID=S0022169423001713
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0022-1694&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0022-1694&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0022-1694&client=summon