{"id":"https://openalex.org/W4386140139","doi":"https://doi.org/10.1080/17538947.2023.2249863","title":"Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms","display_name":"Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms","publication_year":2023,"publication_date":"2023-08-23","ids":{"openalex":"https://openalex.org/W4386140139","doi":"https://doi.org/10.1080/17538947.2023.2249863"},"language":"en","primary_location":{"id":"doi:10.1080/17538947.2023.2249863","is_oa":true,"landing_page_url":"https://doi.org/10.1080/17538947.2023.2249863","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2249863?needAccess=true&role=button","source":{"id":"https://openalex.org/S199162493","display_name":"International Journal of Digital Earth","issn_l":"1753-8947","issn":["1753-8947","1753-8955"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Digital Earth","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2249863?needAccess=true&role=button","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071179839","display_name":"Zhuo Chen","orcid":"https://orcid.org/0000-0003-4466-3992"},"institutions":[{"id":"https://openalex.org/I37574244","display_name":"Sichuan Agricultural University","ror":"https://ror.org/0388c3403","country_code":"CN","type":"education","lineage":["https://openalex.org/I37574244"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuo Chen","raw_affiliation_strings":["College of Civil Engineering, Sichuan Agricultural University, Dujiangyan, People\u2019s Republic of China","Sichuan Higher Education Engineering Research Center for Disaster Prevention and Mitigation of Village Construction, Sichuan Agricultural University, Dujiangyan, People\u2019s Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Civil Engineering, Sichuan Agricultural University, Dujiangyan, People\u2019s Republic of China","institution_ids":["https://openalex.org/I37574244"]},{"raw_affiliation_string":"Sichuan Higher Education Engineering Research Center for Disaster Prevention and Mitigation of Village Construction, Sichuan Agricultural University, Dujiangyan, People\u2019s Republic of China","institution_ids":["https://openalex.org/I37574244"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050017477","display_name":"Danqing Song","orcid":"https://orcid.org/0000-0003-1015-9544"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Danqing Song","raw_affiliation_strings":["School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, People\u2019s Republic of China","State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, People\u2019s Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, People\u2019s Republic of China","institution_ids":["https://openalex.org/I90610280"]},{"raw_affiliation_string":"State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, People\u2019s Republic of China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5050017477"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":{"value":2390,"currency":"USD","value_usd":2390},"apc_paid":{"value":2390,"currency":"USD","value_usd":2390},"fwci":14.1879,"has_fulltext":true,"cited_by_count":38,"citation_normalized_percentile":{"value":0.9890262,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"16","issue":"1","first_page":"3384","last_page":"3416"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10535","display_name":"Landslides and related hazards","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/T10535","display_name":"Landslides and related hazards","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/T10930","display_name":"Flood Risk Assessment and Management","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T11046","display_name":"Geotechnical Engineering and Analysis","score":0.9822999835014343,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7791368961334229},{"id":"https://openalex.org/keywords/landslide","display_name":"Landslide","score":0.6438418030738831},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5699284076690674},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5285177230834961},{"id":"https://openalex.org/keywords/metaheuristic","display_name":"Metaheuristic","score":0.5008704662322998},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.47057053446769714},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.4634874165058136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4317833185195923},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3522496819496155},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3487185835838318},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30631300806999207},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.20914801955223083},{"id":"https://openalex.org/keywords/geomorphology","display_name":"Geomorphology","score":0.1199415922164917}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7791368961334229},{"id":"https://openalex.org/C186295008","wikidata":"https://www.wikidata.org/wiki/Q167903","display_name":"Landslide","level":2,"score":0.6438418030738831},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5699284076690674},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5285177230834961},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.5008704662322998},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.47057053446769714},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4634874165058136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4317833185195923},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3522496819496155},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3487185835838318},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30631300806999207},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.20914801955223083},{"id":"https://openalex.org/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"score":0.1199415922164917}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/17538947.2023.2249863","is_oa":true,"landing_page_url":"https://doi.org/10.1080/17538947.2023.2249863","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2249863?needAccess=true&role=button","source":{"id":"https://openalex.org/S199162493","display_name":"International Journal of Digital Earth","issn_l":"1753-8947","issn":["1753-8947","1753-8955"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Digital Earth","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:61fc8822be8d42e2945056a0f9f27c36","is_oa":true,"landing_page_url":"https://doaj.org/article/61fc8822be8d42e2945056a0f9f27c36","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Digital Earth, Vol 16, Iss 1, Pp 3384-3416 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/17538947.2023.2249863","is_oa":true,"landing_page_url":"https://doi.org/10.1080/17538947.2023.2249863","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2249863?needAccess=true&role=button","source":{"id":"https://openalex.org/S199162493","display_name":"International Journal of Digital Earth","issn_l":"1753-8947","issn":["1753-8947","1753-8955"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Digital Earth","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.7799999713897705}],"awards":[{"id":"https://openalex.org/G3780281819","display_name":null,"funder_award_id":"52109125","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5897435122","display_name":null,"funder_award_id":"2020M680583","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G6096148514","display_name":null,"funder_award_id":"BX20200191","funder_id":"https://openalex.org/F4320335768","funder_display_name":"National Postdoctoral Program for Innovative Talents"},{"id":"https://openalex.org/G7691604384","display_name":null,"funder_award_id":"52208359","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"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320335768","display_name":"National Postdoctoral Program for Innovative Talents","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4386140139.pdf"},"referenced_works_count":85,"referenced_works":["https://openalex.org/W1979408254","https://openalex.org/W2012118327","https://openalex.org/W2046358685","https://openalex.org/W2054512946","https://openalex.org/W2147555471","https://openalex.org/W2200394127","https://openalex.org/W2221487567","https://openalex.org/W2346174874","https://openalex.org/W2499171049","https://openalex.org/W2513587629","https://openalex.org/W2579180916","https://openalex.org/W2579321819","https://openalex.org/W2774595919","https://openalex.org/W2775745878","https://openalex.org/W2793831793","https://openalex.org/W2803545965","https://openalex.org/W2805531306","https://openalex.org/W2805797923","https://openalex.org/W2884613110","https://openalex.org/W2892746236","https://openalex.org/W2905203181","https://openalex.org/W2911084988","https://openalex.org/W2912323688","https://openalex.org/W2915483120","https://openalex.org/W2970545080","https://openalex.org/W2980376317","https://openalex.org/W2981581709","https://openalex.org/W2990262107","https://openalex.org/W2996089053","https://openalex.org/W2999310044","https://openalex.org/W3014673353","https://openalex.org/W3032913569","https://openalex.org/W3036091573","https://openalex.org/W3041778588","https://openalex.org/W3044090919","https://openalex.org/W3048285196","https://openalex.org/W3048311949","https://openalex.org/W3087192809","https://openalex.org/W3087802838","https://openalex.org/W3088698272","https://openalex.org/W3112521739","https://openalex.org/W3112585178","https://openalex.org/W3135457784","https://openalex.org/W3143009190","https://openalex.org/W3144445321","https://openalex.org/W3153854317","https://openalex.org/W3159603761","https://openalex.org/W3169157623","https://openalex.org/W3198956479","https://openalex.org/W3212797097","https://openalex.org/W4200108305","https://openalex.org/W4200538893","https://openalex.org/W4200539666","https://openalex.org/W4200602454","https://openalex.org/W4211011291","https://openalex.org/W4213101497","https://openalex.org/W4213266882","https://openalex.org/W4220656524","https://openalex.org/W4220821905","https://openalex.org/W4223613775","https://openalex.org/W4224092589","https://openalex.org/W4226551898","https://openalex.org/W4229013564","https://openalex.org/W4251883142","https://openalex.org/W4281687867","https://openalex.org/W4281750101","https://openalex.org/W4282559859","https://openalex.org/W4283018661","https://openalex.org/W4283793887","https://openalex.org/W4286383257","https://openalex.org/W4292678510","https://openalex.org/W4293419544","https://openalex.org/W4295009512","https://openalex.org/W4304889311","https://openalex.org/W4306386084","https://openalex.org/W4307722364","https://openalex.org/W4308157882","https://openalex.org/W4312204853","https://openalex.org/W4313420531","https://openalex.org/W4318683761","https://openalex.org/W4321083077","https://openalex.org/W4321601933","https://openalex.org/W4322486759","https://openalex.org/W4323430151","https://openalex.org/W4361290413"],"related_works":["https://openalex.org/W2389676928","https://openalex.org/W3169474304","https://openalex.org/W2369104181","https://openalex.org/W3201652628","https://openalex.org/W4212972401","https://openalex.org/W2389287188","https://openalex.org/W70668483","https://openalex.org/W2885606342","https://openalex.org/W2950100253","https://openalex.org/W3081499580"],"abstract_inverted_index":{"Landslides":[0],"are":[1,21],"one":[2],"of":[3,27,70,77,124,139,151,157],"the":[4,24,71,75,78,85,98,113,118,121,125,140,143,154,158,172,177,181,185],"most":[5],"common":[6],"geological":[7],"hazards":[8],"worldwide,":[9],"especially":[10],"in":[11,51,64,74],"Sichuan":[12,52],"Province":[13,53],"(Southwest":[14],"China).":[15],"The":[16,68,91,168],"current":[17],"study's":[18],"main":[19],"purposes":[20],"to":[22,109],"explore":[23],"potential":[25],"applications":[26],"convolutional":[28],"neural":[29],"networks":[30],"(CNN)":[31],"hybrid":[32],"ensemble":[33],"metaheuristic":[34],"optimization":[35,40,44],"algorithms,":[36],"namely":[37],"beluga":[38],"whale":[39],"(BWO)":[41],"and":[42,103,111,128,134,160,165,180],"coati":[43],"algorithm":[45],"(COA),":[46],"for":[47,189],"landslide":[48,59,79,190],"susceptibility":[49,191],"mapping":[50],"(China).":[54],"For":[55,117],"this":[56],"aim,":[57],"fourteen":[58],"conditioning":[60,72],"factors":[61,73],"were":[62,107,131,163],"compiled":[63],"a":[65,147],"spatial":[66],"database.":[67],"effectiveness":[69],"development":[76],"predictive":[80],"model":[81,145],"was":[82],"quantified":[83],"using":[84],"linear":[86],"support":[87],"vector":[88],"machine":[89],"model.":[90],"receiver":[92],"operating":[93],"characteristic":[94],"(ROC)":[95],"curve":[96],"(AUC),":[97],"root":[99],"mean":[100],"square":[101],"error,":[102],"six":[104],"statistical":[105],"indices":[106],"used":[108],"test":[110],"compare":[112],"three":[114],"resultant":[115],"models.":[116],"training":[119],"dataset,":[120,142],"AUC":[122,149,155],"values":[123,156],"CNN-COA,":[126],"CNN-BWO":[127,159,178],"CNN":[129,161,182],"models":[130,162],"0.946,":[132],"0.937":[133],"0.855,":[135],"respectively.":[136,167],"In":[137],"terms":[138],"validation":[141],"CNN-COA":[144,173],"exhibited":[146],"higher":[148],"value":[150],"0.919,":[152],"while":[153],"0.906":[164],"0.805,":[166],"results":[169],"indicate":[170],"that":[171],"model,":[174,179,183],"followed":[175],"by":[176],"offers":[184],"best":[186],"overall":[187],"performance":[188],"analysis.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":5}],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2025-10-10T00:00:00"}
