{"id":"https://openalex.org/W2983680639","doi":"https://doi.org/10.1109/igarss.2019.8898208","title":"Comparison of Different Machine Learning Models For Landslide Susceptibility Mapping","display_name":"Comparison of Different Machine Learning Models For Landslide Susceptibility Mapping","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2983680639","doi":"https://doi.org/10.1109/igarss.2019.8898208","mag":"2983680639"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8898208","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898208","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5081505126","display_name":"Yaning Yi","orcid":"https://orcid.org/0000-0002-2653-8920"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaning Yi","raw_affiliation_strings":["Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100332498","display_name":"Zhijie Zhang","orcid":"https://orcid.org/0000-0002-7276-5649"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhijie Zhang","raw_affiliation_strings":["Department of Geography, University of Connecticut, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography, University of Connecticut, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050357549","display_name":"Wanchang Zhang","orcid":"https://orcid.org/0000-0002-2607-4628"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanchang Zhang","raw_affiliation_strings":["Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068461949","display_name":"Chi Xu","orcid":"https://orcid.org/0000-0002-0237-0254"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chi Xu","raw_affiliation_strings":["Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"9318","last_page":"9321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10535","display_name":"Landslides and related hazards","score":1.0,"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":1.0,"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/T10644","display_name":"Cryospheric studies and observations","score":0.965399980545044,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":0.9552000164985657,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/landslide","display_name":"Landslide","score":0.8597228527069092},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7783514857292175},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.7066951394081116},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5750276446342468},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.527278482913971},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4703904390335083},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4392359256744385},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.42224055528640747},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38931387662887573},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2800705134868622},{"id":"https://openalex.org/keywords/geomorphology","display_name":"Geomorphology","score":0.14117524027824402}],"concepts":[{"id":"https://openalex.org/C186295008","wikidata":"https://www.wikidata.org/wiki/Q167903","display_name":"Landslide","level":2,"score":0.8597228527069092},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7783514857292175},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7066951394081116},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5750276446342468},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.527278482913971},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4703904390335083},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4392359256744385},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.42224055528640747},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38931387662887573},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2800705134868622},{"id":"https://openalex.org/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"score":0.14117524027824402}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8898208","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898208","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2029816621","https://openalex.org/W2036881582","https://openalex.org/W2049271682","https://openalex.org/W2064319214","https://openalex.org/W2080134555","https://openalex.org/W2088730795","https://openalex.org/W2221487567","https://openalex.org/W2489814317","https://openalex.org/W2593298971","https://openalex.org/W2622828109","https://openalex.org/W2627821436","https://openalex.org/W2774595919","https://openalex.org/W2783350994","https://openalex.org/W2791665776","https://openalex.org/W2793831793","https://openalex.org/W2912323688"],"related_works":["https://openalex.org/W4327772909","https://openalex.org/W4384828018","https://openalex.org/W4312478656","https://openalex.org/W3186233728","https://openalex.org/W4364301914","https://openalex.org/W2059074807","https://openalex.org/W4205958290","https://openalex.org/W4226490104","https://openalex.org/W3195168932","https://openalex.org/W1996541855"],"abstract_inverted_index":{"The":[0,70],"main":[1],"objective":[2],"of":[3,38,43,147,160],"this":[4],"study":[5,68,140],"is":[6],"to":[7,61,132],"compare":[8],"and":[9,23,54,73,123,136],"evaluate":[10],"three":[11,90],"different":[12],"machine":[13,26,105],"learning":[14,106],"models,":[15,107],"namely":[16],"logistic":[17],"regression":[18],"(LR),":[19],"Na\u00efve":[20],"Bayes":[21],"(NB)":[22],"support":[24],"vector":[25],"(SVM),":[27],"for":[28,66,150],"landslide":[29,64,85,151],"susceptibility":[30,65,86,152],"mapping":[31,153],"at":[32,120,154],"the":[33,63,67,84,94,98,102,110,117,121,124,128,155],"Jiuzhaigou":[34,156],"area,":[35,157],"Sichuan":[36,158],"Province":[37,159],"China.":[39,161],"A":[40],"total":[41],"number":[42],"917":[44],"landslides":[45],"visually":[46],"interpreted":[47],"from":[48,77,89],"high":[49],"resolution":[50],"remotely":[51],"sensed":[52],"imaginaries":[53],"thirteen":[55],"selected":[56],"causing":[57],"factors":[58],"were":[59,92],"used":[60],"model":[62,100,119,126,149],"region.":[69],"success":[71,134],"rates":[72,75,135],"prediction":[74,137],"derived":[76],"Receiver":[78],"Operating":[79],"Characteristic":[80],"curve":[81],"analysis":[82],"on":[83],"maps":[87],"obtained":[88],"models":[91],"investigated,":[93],"results":[95],"suggested":[96],"that":[97],"LR":[99],"outperformed":[101],"other":[103],"two":[104],"ranked":[108],"in":[109,145],"top":[111],"concerning":[112],"general":[113],"performance,":[114],"followed":[115],"by":[116],"NB":[118],"second,":[122],"SVM":[125],"had":[127],"lowest":[129],"accuracies,":[130],"regarding":[131],"both":[133],"rates.":[138],"This":[139],"provided":[141],"a":[142],"new":[143],"perspective":[144],"selection":[146],"feasible":[148]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
