{"id":"https://openalex.org/W4416250522","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229154","title":"Groundwater Seepage Modeling in a River-Canal System based on Physics-Informed Neural Networks","display_name":"Groundwater Seepage Modeling in a River-Canal System based on Physics-Informed Neural Networks","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250522","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229154"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229154","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229154","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100348771","display_name":"Chong Chen","orcid":"https://orcid.org/0000-0001-6164-5492"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chong Chen","raw_affiliation_strings":["China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026193151","display_name":"Yifan Li","orcid":"https://orcid.org/0009-0008-1026-3636"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Li","raw_affiliation_strings":["China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102011889","display_name":"Zhiwei Han","orcid":"https://orcid.org/0000-0002-8344-7320"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongyu Han","raw_affiliation_strings":["China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101316171","display_name":"Yixiao Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixiao Niu","raw_affiliation_strings":["China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017533636","display_name":"Xiaoyu Zhu","orcid":"https://orcid.org/0000-0002-6815-3754"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Zhu","raw_affiliation_strings":["China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041778411","display_name":"Yaru Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaru Xue","raw_affiliation_strings":["China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum (Beijing),College of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I204553293"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100348771"],"corresponding_institution_ids":["https://openalex.org/I204553293"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31515805,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10894","display_name":"Groundwater flow and contamination studies","score":0.40880000591278076,"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/T10894","display_name":"Groundwater flow and contamination studies","score":0.40880000591278076,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.13420000672340393,"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/T12293","display_name":"Dam Engineering and Safety","score":0.08340000361204147,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/groundwater","display_name":"Groundwater","score":0.7757999897003174},{"id":"https://openalex.org/keywords/hydrogeology","display_name":"Hydrogeology","score":0.7335000038146973},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6917999982833862},{"id":"https://openalex.org/keywords/hydraulic-conductivity","display_name":"Hydraulic conductivity","score":0.6176999807357788},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5554999709129333},{"id":"https://openalex.org/keywords/groundwater-flow","display_name":"Groundwater flow","score":0.5339000225067139},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.47440001368522644},{"id":"https://openalex.org/keywords/groundwater-model","display_name":"Groundwater model","score":0.44040000438690186}],"concepts":[{"id":"https://openalex.org/C76177295","wikidata":"https://www.wikidata.org/wiki/Q161598","display_name":"Groundwater","level":2,"score":0.7757999897003174},{"id":"https://openalex.org/C33556824","wikidata":"https://www.wikidata.org/wiki/Q179509","display_name":"Hydrogeology","level":2,"score":0.7335000038146973},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6917999982833862},{"id":"https://openalex.org/C63184880","wikidata":"https://www.wikidata.org/wiki/Q2783041","display_name":"Hydraulic conductivity","level":3,"score":0.6176999807357788},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5554999709129333},{"id":"https://openalex.org/C131227075","wikidata":"https://www.wikidata.org/wiki/Q5611028","display_name":"Groundwater flow","level":4,"score":0.5339000225067139},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.47440001368522644},{"id":"https://openalex.org/C176650113","wikidata":"https://www.wikidata.org/wiki/Q370689","display_name":"Groundwater model","level":5,"score":0.44040000438690186},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37860000133514404},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3476000130176544},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3276999890804291},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C39769621","wikidata":"https://www.wikidata.org/wiki/Q3342272","display_name":"Water table","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.262800008058548},{"id":"https://openalex.org/C76886044","wikidata":"https://www.wikidata.org/wiki/Q2883300","display_name":"Hydrology (agriculture)","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229154","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229154","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322880","display_name":"Natural Science Foundation of Gansu Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1531958500","https://openalex.org/W2072826040","https://openalex.org/W2100495367","https://openalex.org/W2103496339","https://openalex.org/W2133190781","https://openalex.org/W2618530766","https://openalex.org/W2773786476","https://openalex.org/W2791992510","https://openalex.org/W2794371820","https://openalex.org/W2800893514","https://openalex.org/W2899283552","https://openalex.org/W2993987425","https://openalex.org/W3006689658","https://openalex.org/W3009752764","https://openalex.org/W3045938462","https://openalex.org/W3047035577","https://openalex.org/W3104994177","https://openalex.org/W3137392741","https://openalex.org/W3155119506","https://openalex.org/W3161547358","https://openalex.org/W3168141280","https://openalex.org/W4200089431","https://openalex.org/W4205143825","https://openalex.org/W4281711890","https://openalex.org/W4309763689","https://openalex.org/W4322741220","https://openalex.org/W4367677599","https://openalex.org/W4377291919","https://openalex.org/W4379882595","https://openalex.org/W4382652314","https://openalex.org/W4385858045","https://openalex.org/W4386101331","https://openalex.org/W4386571111"],"related_works":[],"abstract_inverted_index":{"Neural":[0,46],"networks,":[1],"especially":[2],"deep":[3],"learning,":[4],"have":[5],"achieved":[6],"revolutionary":[7],"advances":[8],"in":[9,37,139,162,189,195],"several":[10],"domains,":[11],"including":[12],"image":[13],"and":[14,29,41,69,89,95,116,159,173],"speech":[15],"recognition,":[16],"with":[17,31,57,122],"excellent":[18],"results.":[19],"However,":[20],"their":[21,35],"reliance":[22],"on":[23,63,104],"labeled":[24],"data,":[25],"lack":[26],"of":[27,87,131,133,181,183],"interpretability,":[28],"inconsistency":[30],"physical":[32,55],"principles":[33],"limit":[34],"applicability":[36,138],"groundwater":[38,66,74,142,156,198],"seepage":[39,75,120,143,157,164,199],"prediction":[40],"other":[42],"scientific":[43],"disciplines.":[44],"Physics-Informed":[45],"Networks":[47],"(PINNs)":[48],"significantly":[49],"improve":[50],"these":[51],"issues":[52],"by":[53,83],"integrating":[54],"knowledge":[56],"neural":[58],"networks.":[59],"This":[60,78,152],"study":[61,191],"focuses":[62],"modeling":[64],"the":[65,109,112],"flow":[67],"field":[68,170],"proposes":[70],"a":[71,128,178],"physics-informed":[72],"river-canal":[73],"model":[76,79,153,175],"(PI-RGSM).":[77],"enables":[80],"self-supervised":[81],"learning":[82],"incorporating":[84],"hard":[85],"constraints":[86],"boundary":[88,96],"initial":[90],"conditions,":[91],"utilizing":[92],"hydrogeological":[93],"parameters":[94],"conditions":[97],"as":[98],"direct":[99],"inputs,":[100],"thus":[101],"diminishing":[102],"dependence":[103],"observable":[105],"data.":[106],"Compared":[107],"to":[108,115],"baseline":[110],"PINNs,":[111],"PI-RGSM":[113],"adapts":[114],"accurately":[117],"predicts":[118],"diverse":[119],"situations":[121],"just":[123],"one":[124],"training":[125],"session,":[126],"achieving":[127],"mean":[129,179],"coefficient":[130,180],"determination":[132,182],"0.978.":[134],"To":[135],"further":[136],"enhance":[137],"complex":[140,163],"dynamic":[141],"situations,":[144],"we":[145],"propose":[146],"PI-RGSM-K,":[147],"which":[148],"builds":[149],"upon":[150],"PI-RGSM.":[151],"simulates":[154],"heterogeneous":[155],"fields":[158],"improves":[160],"performance":[161],"environments":[165],"through":[166],"parameterized":[167],"hydraulic":[168],"conductivity":[169],"K(x,":[171],"y)":[172],"fine-adjusted":[174],"architecture,":[176],"attaining":[177],"0.982.":[184],"The":[185],"PINN":[186],"models":[187],"proposed":[188],"this":[190],"demonstrate":[192],"exceptional":[193],"efficacy":[194],"precisely":[196],"forecasting":[197],"behavior.":[200]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}
