{"id":"https://openalex.org/W4408810173","doi":"https://doi.org/10.1007/s10462-025-11192-z","title":"Multi-strategy improved snow ablation optimizer: a case study of optimization of kernel extreme learning machine for flood prediction","display_name":"Multi-strategy improved snow ablation optimizer: a case study of optimization of kernel extreme learning machine for flood prediction","publication_year":2025,"publication_date":"2025-03-24","ids":{"openalex":"https://openalex.org/W4408810173","doi":"https://doi.org/10.1007/s10462-025-11192-z"},"language":"en","primary_location":{"id":"doi:10.1007/s10462-025-11192-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-025-11192-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10462-025-11192-z.pdf","source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"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":"Artificial Intelligence Review","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10462-025-11192-z.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066957313","display_name":"Lele Cui","orcid":"https://orcid.org/0000-0002-9408-4916"},"institutions":[{"id":"https://openalex.org/I4210115513","display_name":"Xi\u2019an University","ror":"https://ror.org/01zzmf129","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210115513"]},{"id":"https://openalex.org/I4210131919","display_name":"Xi'an University of Technology","ror":"https://ror.org/038avdt50","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131919"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lele Cui","raw_affiliation_strings":["Department of Applied Mathematics, Xi\u2019an University of Technology, Xi\u2019an, 710054, PR China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Xi\u2019an University of Technology, Xi\u2019an, 710054, PR China","institution_ids":["https://openalex.org/I4210115513","https://openalex.org/I4210131919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101455287","display_name":"Gang Hu","orcid":"https://orcid.org/0000-0003-4916-3460"},"institutions":[{"id":"https://openalex.org/I4210115513","display_name":"Xi\u2019an University","ror":"https://ror.org/01zzmf129","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210115513"]},{"id":"https://openalex.org/I4210131919","display_name":"Xi'an University of Technology","ror":"https://ror.org/038avdt50","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131919"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Hu","raw_affiliation_strings":["Department of Applied Mathematics, Xi\u2019an University of Technology, Xi\u2019an, 710054, PR China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Xi\u2019an University of Technology, Xi\u2019an, 710054, PR China","institution_ids":["https://openalex.org/I4210115513","https://openalex.org/I4210131919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110031114","display_name":"Yaolin Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yaolin Zhu","raw_affiliation_strings":["School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an, China","institution_ids":["https://openalex.org/I27599042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5110031114"],"corresponding_institution_ids":["https://openalex.org/I27599042"],"apc_list":{"value":3090,"currency":"USD","value_usd":3090},"apc_paid":{"value":3090,"currency":"USD","value_usd":3090},"fwci":3.5694,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.92778127,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"58","issue":"6","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9972000122070312,"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":0.9972000122070312,"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/T12676","display_name":"Machine Learning and ELM","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/flood-myth","display_name":"Flood myth","score":0.7162265181541443},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.7130393385887146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7053283452987671},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5761027932167053},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5108256340026855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49249324202537537},{"id":"https://openalex.org/keywords/snow","display_name":"Snow","score":0.46800723671913147},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.42365455627441406},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13082125782966614},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09777393937110901},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09602847695350647}],"concepts":[{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.7162265181541443},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.7130393385887146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7053283452987671},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5761027932167053},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5108256340026855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49249324202537537},{"id":"https://openalex.org/C197046000","wikidata":"https://www.wikidata.org/wiki/Q7561","display_name":"Snow","level":2,"score":0.46800723671913147},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.42365455627441406},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13082125782966614},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09777393937110901},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09602847695350647},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0},{"id":"https://openalex.org/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10462-025-11192-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-025-11192-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10462-025-11192-z.pdf","source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"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":"Artificial Intelligence Review","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10462-025-11192-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-025-11192-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10462-025-11192-z.pdf","source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"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":"Artificial Intelligence Review","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Climate action","score":0.8799999952316284,"id":"https://metadata.un.org/sdg/13"}],"awards":[{"id":"https://openalex.org/G2597632770","display_name":null,"funder_award_id":"Grants No. 52375264","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4408810173.pdf"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W1965568221","https://openalex.org/W1975436849","https://openalex.org/W2001422417","https://openalex.org/W2004133169","https://openalex.org/W2031183907","https://openalex.org/W2033011996","https://openalex.org/W2061438946","https://openalex.org/W2075108083","https://openalex.org/W2100386237","https://openalex.org/W2152195021","https://openalex.org/W2290883490","https://openalex.org/W2345115205","https://openalex.org/W2726592172","https://openalex.org/W2729414995","https://openalex.org/W2893066337","https://openalex.org/W2898385607","https://openalex.org/W3048917786","https://openalex.org/W3152970886","https://openalex.org/W3188111058","https://openalex.org/W4210565175","https://openalex.org/W4212993228","https://openalex.org/W4254460084","https://openalex.org/W4281687867","https://openalex.org/W4285191770","https://openalex.org/W4285810043","https://openalex.org/W4307722364","https://openalex.org/W4307911706","https://openalex.org/W4310004251","https://openalex.org/W4313563685","https://openalex.org/W4317629012","https://openalex.org/W4320487090","https://openalex.org/W4323922821","https://openalex.org/W4362665748","https://openalex.org/W4365141441","https://openalex.org/W4384701513","https://openalex.org/W4385188679","https://openalex.org/W4386244828","https://openalex.org/W4386400636","https://openalex.org/W4387095893","https://openalex.org/W4387739578","https://openalex.org/W4388188257","https://openalex.org/W4389636822","https://openalex.org/W4389856977","https://openalex.org/W4390128809","https://openalex.org/W4390510014","https://openalex.org/W4391617477","https://openalex.org/W4392130001","https://openalex.org/W4396529649","https://openalex.org/W4396623743","https://openalex.org/W4398224813","https://openalex.org/W4399559606","https://openalex.org/W4399589714","https://openalex.org/W4399622528","https://openalex.org/W4399920786","https://openalex.org/W4400076714","https://openalex.org/W4402943703","https://openalex.org/W4404797940"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2968645206","https://openalex.org/W2556335056","https://openalex.org/W2002678693","https://openalex.org/W2743832667","https://openalex.org/W2969890106"],"abstract_inverted_index":{"The":[0],"Kernel":[1],"Extreme":[2],"Learning":[3],"Machine":[4],"(KELM)":[5],"has":[6,255],"the":[7,50,65,133,143,147,156,171,181,196,200,223,238,275],"advantage":[8],"of":[9,67,142,149,158,168,175,184,203,225,240,277],"automatically":[10],"extracting":[11],"data":[12],"features,":[13],"learning":[14,105,117],"and":[15,35,113,137,189,207,211,235,246,266,273,281],"processing":[16],"nonlinear":[17],"problems":[18,32,163],"from":[19],"historical":[20,107],"data,":[21],"which":[22],"can":[23,236,250,262],"help":[24],"achieve":[25],"better":[26,79],"prediction":[27,31,41,162,182,192,209,216,227,261],"results":[28,174,183],"for":[29,63,92,155],"flood":[30,39,55,68,161,241,260],"with":[33],"complex":[34,51,245],"sudden":[36],"causes.":[37],"Traditional":[38],"disaster":[40],"usually":[42],"only":[43],"considers":[44],"one":[45],"influencing":[46,73,233],"factor":[47,234],"without":[48],"considering":[49],"factors":[52],"that":[53,253],"affect":[54],"occurrence.":[56],"This":[57,220],"article":[58],"develops":[59],"a":[60,97,150,231],"new":[61],"method":[62,177,221],"predicting":[64],"probability":[66,239],"occurrence":[69,242],"based":[70,99,109,229,243],"on":[71,132,230,244,279],"20":[72],"factors.":[74,248],"Firstly,":[75],"in":[76,160,269],"order":[77],"to":[78,128,180,195,214,271],"utilize":[80],"KELM":[81,144,206],"performance,":[82],"an":[83],"improved":[84],"snow":[85,120],"ablation":[86,121],"optimization":[87,122,131],"algorithm":[88],"(MESAO)":[89],"was":[90,164],"proposed":[91],"subsequent":[93],"experiments":[94],"by":[95],"introducing":[96],"level":[98],"selection":[100],"pressure":[101],"mechanism,":[102],"covariance":[103],"matrix":[104],"strategy,":[106,112],"position":[108],"boundary":[110],"adjustment":[111],"random":[114],"centroid":[115],"reverse":[116],"strategy":[118],"into":[119],"(SAO).":[123],"Secondly,":[124],"MESAO":[125,204],"is":[126],"used":[127],"perform":[129],"hyperparameter":[130,169],"regularization":[134],"coefficient":[135],"C":[136],"kernel":[138],"function":[139],"parameter":[140],"S":[141],"model.":[145],"Finally,":[146],"construction":[148],"multi":[151],"feature":[152],"input\u2013output":[153],"model":[154],"application":[157],"MESAO-KELM":[159,254],"completed.":[165],"In":[166],"terms":[167],"optimization,":[170],"numerical":[172],"experimental":[173],"this":[176],"were":[178,218],"superior":[179],"10":[185],"other":[186,215],"intelligent":[187],"algorithms":[188],"5":[190],"regression":[191],"models.":[193],"According":[194],"evaluation":[197],"index":[198],"results,":[199],"best":[201],"adaptability":[202],"optimized":[205],"higher":[208],"accuracy":[210],"stability":[212],"compared":[213],"models":[217,228],"demonstrated.":[219],"overcomes":[222],"limitations":[224],"traditional":[226],"single":[232],"predict":[237],"variable":[247],"It":[249],"be":[251],"said":[252],"strong":[256],"generalization":[257],"ability.":[258],"Accurate":[259],"provide":[263],"early":[264],"warning":[265],"take":[267],"measures":[268],"advance":[270],"protect":[272],"reduce":[274],"impact":[276],"floods":[278],"human":[280],"social":[282],"development.":[283]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":5}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}