{"id":"https://openalex.org/W7162668901","doi":"https://doi.org/10.1016/j.ecoinf.2026.103830","title":"Predictive capabilities of novel deep neural networks learning for long-term streamflow prediction: Insights from the Barandouz Chay River","display_name":"Predictive capabilities of novel deep neural networks learning for long-term streamflow prediction: Insights from the Barandouz Chay River","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7162668901","doi":"https://doi.org/10.1016/j.ecoinf.2026.103830"},"language":"en","primary_location":{"id":"doi:10.1016/j.ecoinf.2026.103830","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ecoinf.2026.103830","pdf_url":null,"source":{"id":"https://openalex.org/S195809937","display_name":"Ecological Informatics","issn_l":"1574-9541","issn":["1574-9541","1878-0512"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ecological Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.ecoinf.2026.103830","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067787293","display_name":"Edris Merufinia","orcid":null},"institutions":[{"id":"https://openalex.org/I136830121","display_name":"Islamic Azad University South Tehran Branch","ror":"https://ror.org/02xc21a77","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I136830121"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Edris Merufinia","raw_affiliation_strings":["Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I136830121"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000307425","display_name":"Ahmad Sharafati","orcid":"https://orcid.org/0000-0003-0448-2871"},"institutions":[{"id":"https://openalex.org/I136830121","display_name":"Islamic Azad University South Tehran Branch","ror":"https://ror.org/02xc21a77","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I136830121"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Ahmad Sharafati","raw_affiliation_strings":["Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I136830121"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088259535","display_name":"Hirad Abghari","orcid":"https://orcid.org/0000-0002-3407-3297"},"institutions":[{"id":"https://openalex.org/I38476204","display_name":"Urmia University","ror":"https://ror.org/032fk0x53","country_code":"IR","type":"education","lineage":["https://openalex.org/I38476204"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Hirad Abghari","raw_affiliation_strings":["Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Iran","institution_ids":["https://openalex.org/I38476204"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5137278462","display_name":"Yousef Hassanzadeh","orcid":null},"institutions":[{"id":"https://openalex.org/I41832843","display_name":"University of Tabriz","ror":"https://ror.org/01papkj44","country_code":"IR","type":"education","lineage":["https://openalex.org/I41832843"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Yousef Hassanzadeh","raw_affiliation_strings":["Department of Water Engineering, Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Water Engineering, Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran","institution_ids":["https://openalex.org/I41832843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5000307425"],"corresponding_institution_ids":["https://openalex.org/I136830121"],"apc_list":{"value":2510,"currency":"USD","value_usd":2510},"apc_paid":{"value":2510,"currency":"USD","value_usd":2510},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.62325823,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"96","issue":null,"first_page":"103830","last_page":"103830"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.5927000045776367,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.5927000045776367,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10302","display_name":"Fish Ecology and Management Studies","score":0.06369999796152115,"subfield":{"id":"https://openalex.org/subfields/2309","display_name":"Nature and Landscape Conservation"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14177","display_name":"Water Resources and Management","score":0.031199999153614044,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6144999861717224},{"id":"https://openalex.org/keywords/streamflow","display_name":"Streamflow","score":0.6141999959945679},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.3901999890804291},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.37209999561309814},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.3346000015735626},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3142000138759613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6370999813079834},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6144999861717224},{"id":"https://openalex.org/C53739315","wikidata":"https://www.wikidata.org/wiki/Q29425295","display_name":"Streamflow","level":3,"score":0.6141999959945679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.600600004196167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4636000096797943},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.3901999890804291},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3781999945640564},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3346000015735626},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2556000053882599},{"id":"https://openalex.org/C2993741502","wikidata":"https://www.wikidata.org/wiki/Q1187134","display_name":"River management","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.ecoinf.2026.103830","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ecoinf.2026.103830","pdf_url":null,"source":{"id":"https://openalex.org/S195809937","display_name":"Ecological Informatics","issn_l":"1574-9541","issn":["1574-9541","1878-0512"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ecological Informatics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.ecoinf.2026.103830","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ecoinf.2026.103830","pdf_url":null,"source":{"id":"https://openalex.org/S195809937","display_name":"Ecological Informatics","issn_l":"1574-9541","issn":["1574-9541","1878-0512"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ecological Informatics","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4884623885154724,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W754702840","https://openalex.org/W1864468960","https://openalex.org/W2763520430","https://openalex.org/W2800819102","https://openalex.org/W2809254203","https://openalex.org/W2905485021","https://openalex.org/W2997167441","https://openalex.org/W3016464096","https://openalex.org/W3028230057","https://openalex.org/W3034859966","https://openalex.org/W3158135283","https://openalex.org/W3164646897","https://openalex.org/W3178810976","https://openalex.org/W3196897376","https://openalex.org/W4200366988","https://openalex.org/W4289520402","https://openalex.org/W4296127594","https://openalex.org/W4312130845","https://openalex.org/W4317898772","https://openalex.org/W4322772399","https://openalex.org/W4362709188","https://openalex.org/W4366723045","https://openalex.org/W4390841447","https://openalex.org/W4400590186","https://openalex.org/W4401798908","https://openalex.org/W4402532707","https://openalex.org/W4404837156","https://openalex.org/W4405857186","https://openalex.org/W4408444903","https://openalex.org/W4413036349","https://openalex.org/W4413461012","https://openalex.org/W4414052148","https://openalex.org/W7161031176"],"related_works":[],"abstract_inverted_index":{"Dams":[0],"and":[1,14,64,86,110,118,150,171,185,221,229,259],"reservoirs":[2],"play":[3],"a":[4],"crucial":[5,329],"role":[6,193],"for":[7,35,241],"public":[8],"health,":[9],"food":[10],"security,":[11],"economic":[12],"growth,":[13],"flood":[15],"protection,":[16],"especially":[17],"as":[18,231,233],"climate":[19,341],"change":[20],"challenges":[21],"sustainable":[22],"water":[23],"management.":[24],"This":[25],"study":[26],"explored":[27],"the":[28,43,124,191,299,317],"application":[29],"of":[30,38,72,103,194,216,254,319],"regression-based":[31],"deep":[32,333],"learning":[33,334],"models":[34,47,167,207,335],"long-term":[36],"prediction":[37,90],"Barandouz":[39],"River":[40],"flow.":[41],"In":[42,297],"present":[44],"study,":[45],"four":[46],"were":[48,76,121,239],"used,":[49],"namely,":[50],"Continuous":[51],"Weighted":[52],"Residual":[53],"Method":[54],"(CWERM),":[55],"Physics-Informed":[56],"Koopman":[57],"Networks":[58],"(PI-Koopman),":[59],"Convolutional":[60],"Neural":[61,66],"Network":[62],"(CNN)":[63],"Implicit":[65],"Representations":[67],"(INRs).":[68],"Forty":[69],"years":[70],"(1980\u20132020)":[71],"daily":[73],"time-series":[74],"data":[75],"adopted":[77],",":[78,145],"including":[79],"temperature,":[80],"precipitation,":[81],"relative":[82],"humidity,":[83],"wind":[84],"speed,":[85],"discharge.":[87],"To":[88],"improve":[89],"accuracy,":[91],"lagged":[92],"discharge":[93,196],"(up":[94],"to":[95,112,182,305],"three":[96],"days)":[97],"was":[98,138,249,270],"incorporated.":[99],"Data":[100],"pre-processing":[101],"consisted":[102],"missing":[104],"value":[105],"reconstruction":[106],"(KNN),":[107],"outlier":[108],"removal,":[109],"normalization":[111],"mitigate":[113],"overfitting":[114],"risks.":[115],"Feature":[116],"selection":[117],"scenario":[119,155],"construction":[120],"guided":[122],"by":[123],"Minimum":[125],"Redundancy":[126],"Maximum":[127],"Relevance":[128],"(MRMR)":[129],"method,":[130],"resulted":[131],"in":[132,168,197,208,283,292,336],"five":[133],"predictive":[134,163],"scenarios.":[135],"Model":[136],"evaluation":[137],"based":[139],"on":[140],"multiple":[141],"criteria":[142],"(R":[143],"2":[144,177,214,247,280],"MAE,":[146],"RMSE,":[147],"MAD,":[148],"NSE,":[149],"KGE).":[151],"Results":[152],"showed":[153],"that":[154],"4":[156],"excluding":[157],"historical":[158,312],"flow":[159],"variables":[160],"exhibited":[161],"severe":[162],"failure":[164],"across":[165],"all":[166,205],"both":[169,209],"training":[170],"testing":[172,175,278],"phases,":[173],"with":[174,224],"R":[176,213,246,279],"ranging":[178],"from":[179],"0.087":[180],"(CWERM)":[181],"0.189":[183],"(PI-Koopman)":[184],"NSE":[186,226],"remaining":[187],"below":[188],"0.23,":[189],"highlighting":[190],"critical":[192],"antecedent":[195],"modeling":[198],"hydrological":[199,324],"memory.":[200],"While,":[201],"CNN":[202],"consistently":[203],"outperformed":[204],"other":[206],"phases.":[210],"During":[211],"testing,":[212,245],"values":[215],"0.991":[217],"(SN1),":[218],"0.992":[219],"(SN3),":[220],"0.981":[222],"(SN5),":[223],"corresponding":[225],">":[227],"0.910":[228],"RMSE":[230,265],"low":[232],"4.157":[234],"m":[235,267],"3":[236,268],"/s":[237],"(SN5)":[238],"observed":[240],"CNN.":[242],"Its":[243],"average":[244,264],"(0.977),":[248],"14.0\u201318.3%":[250],"higher":[251],"than":[252],"those":[253],"ANN-OA":[255],"(0.857),":[256],"INRs":[257],"(0.855),":[258],"PI-Koopman":[260],"(0.826),":[261],"while":[262,314],"its":[263],"(7.25":[266],"/s)":[269],"34.5\u201344.1%":[271],"lower.":[272],"PI-Koopman,":[273],"despite":[274],"moderate":[275],"performance":[276],"(best":[277],"=":[281],"0.858":[282],"SN3),":[284],"maintained":[285],"predictions":[286],"within":[287],"physically":[288],"reasonable":[289],"bounds,":[290],"particularly":[291],"degraded":[293],"scenarios":[294],"like":[295],"SN4.":[296],"conclusion,":[298],"results":[300],"demonstrated":[301],"CNN's":[302],"exceptional":[303],"ability":[304],"capture":[306],"complex,":[307],"nonlinear":[308],"streamflow":[309],"dynamics":[310],"under":[311,340],"conditions,":[313],"also":[315],"underscored":[316],"limitations":[318],"purely":[320],"data-driven":[321],"approaches":[322],"when":[323],"stationarity":[325],"is":[326],"violated":[327],"providing":[328],"insights":[330],"into":[331],"deploying":[332],"semi-arid,":[337],"human-influenced":[338],"basins":[339],"uncertainty.":[342]},"counts_by_year":[],"updated_date":"2026-06-06T06:22:57.294733","created_date":"2026-05-29T00:00:00"}
