{"id":"https://openalex.org/W4388773962","doi":"https://doi.org/10.3390/rs15225384","title":"An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping","display_name":"An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping","publication_year":2023,"publication_date":"2023-11-16","ids":{"openalex":"https://openalex.org/W4388773962","doi":"https://doi.org/10.3390/rs15225384"},"language":"en","primary_location":{"id":"doi:10.3390/rs15225384","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15225384","pdf_url":"https://www.mdpi.com/2072-4292/15/22/5384/pdf?version=1700144586","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/15/22/5384/pdf?version=1700144586","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084334876","display_name":"Changpeng Wang","orcid":"https://orcid.org/0000-0001-8603-9704"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changpeng Wang","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108878834","display_name":"Yangchun Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I4210121989","display_name":"Pearl River Hydraulic Research Institute","ror":"https://ror.org/036g8gs02","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210121989"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangchun Lin","raw_affiliation_strings":["China Water Resources Pearl River Planning, Surveying & Designing Co., Ltd., Guangzhou 510611, China","School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China"],"affiliations":[{"raw_affiliation_string":"China Water Resources Pearl River Planning, Surveying & Designing Co., Ltd., Guangzhou 510611, China","institution_ids":["https://openalex.org/I4210121989"]},{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028217752","display_name":"Zhiwen Tao","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwen Tao","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111076219","display_name":"Jiayin Zhan","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayin Zhan","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009530693","display_name":"Wenkai Li","orcid":"https://orcid.org/0000-0001-6548-7882"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenkai Li","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103200364","display_name":"Huabing Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huabing Huang","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5103200364"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":1.6079,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.8278543,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"15","issue":"22","first_page":"5384","last_page":"5384"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":1.0,"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/T11186","display_name":"Hydrology and Drought Analysis","score":0.9940000176429749,"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/T11483","display_name":"Tropical and Extratropical Cyclones Research","score":0.9927999973297119,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/flood-myth","display_name":"Flood myth","score":0.7286055684089661},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6609303951263428},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6186042428016663},{"id":"https://openalex.org/keywords/stratified-sampling","display_name":"Stratified sampling","score":0.5378723740577698},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47736525535583496},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4577248990535736},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.444620817899704},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.3759232461452484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35310640931129456},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3230588436126709},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.22847726941108704},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16913899779319763}],"concepts":[{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.7286055684089661},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6609303951263428},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6186042428016663},{"id":"https://openalex.org/C49898467","wikidata":"https://www.wikidata.org/wiki/Q1517706","display_name":"Stratified sampling","level":2,"score":0.5378723740577698},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47736525535583496},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4577248990535736},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.444620817899704},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3759232461452484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35310640931129456},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3230588436126709},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.22847726941108704},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16913899779319763},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs15225384","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15225384","pdf_url":"https://www.mdpi.com/2072-4292/15/22/5384/pdf?version=1700144586","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c17cf5dbf84a40be8c802473e714f42d","is_oa":true,"landing_page_url":"https://doaj.org/article/c17cf5dbf84a40be8c802473e714f42d","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 15, Iss 22, p 5384 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/22/5384/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15225384","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15225384","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15225384","pdf_url":"https://www.mdpi.com/2072-4292/15/22/5384/pdf?version=1700144586","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.800000011920929,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G2969832438","display_name":null,"funder_award_id":"2022A1515011494","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388773962.pdf"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W1973749534","https://openalex.org/W1993934953","https://openalex.org/W2012738906","https://openalex.org/W2042315239","https://openalex.org/W2153853604","https://openalex.org/W2322078666","https://openalex.org/W2766228856","https://openalex.org/W2771412470","https://openalex.org/W2780363565","https://openalex.org/W2904486608","https://openalex.org/W2916915995","https://openalex.org/W2943939976","https://openalex.org/W2955716717","https://openalex.org/W2959688306","https://openalex.org/W2985766090","https://openalex.org/W2993767981","https://openalex.org/W2996701347","https://openalex.org/W3006114317","https://openalex.org/W3010248316","https://openalex.org/W3036595569","https://openalex.org/W3038030066","https://openalex.org/W3042024008","https://openalex.org/W3081610994","https://openalex.org/W3087236291","https://openalex.org/W3103865021","https://openalex.org/W3109476332","https://openalex.org/W3111111052","https://openalex.org/W3120955013","https://openalex.org/W3150920915","https://openalex.org/W3159742334","https://openalex.org/W3169836903","https://openalex.org/W3175813848","https://openalex.org/W3179924147","https://openalex.org/W3204751889","https://openalex.org/W3204782727","https://openalex.org/W4220775862","https://openalex.org/W4221125467","https://openalex.org/W4223974190","https://openalex.org/W4224991974","https://openalex.org/W4283018661","https://openalex.org/W4283211073","https://openalex.org/W4283574742","https://openalex.org/W4288759981","https://openalex.org/W4380738799","https://openalex.org/W4386541608","https://openalex.org/W4387332415"],"related_works":["https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W2004826645","https://openalex.org/W3135818052","https://openalex.org/W4372048956","https://openalex.org/W4206989953","https://openalex.org/W4283776244"],"abstract_inverted_index":{"Data-driven":[0],"flood":[1,14,20,152,366,374,406],"susceptibility":[2,21,367,378,407],"modeling":[3],"is":[4,58,80,286,400],"an":[5],"efficient":[6],"way":[7],"to":[8,44,243,290,361,384,402,405,409],"map":[9],"the":[10,19,25,29,46,95,98,227,246,270,300,316,327,370,387,395,411],"spatial":[11,76,99,110,414],"distribution":[12],"of":[13,18,36,48,62,65,97,203,224,269,373,413],"likelihood.":[15],"The":[16,34,127,200,233,263,284,338],"quality":[17],"model":[22,217],"relies":[23],"on":[24,109,216],"learning":[26,37,141,220,238,292,341],"technique":[27,342],"and":[28,73,135,167,198,256,280,294,310,322,326,333,353,357,392],"data":[30,49,70,119],"used":[31],"for":[32,275,343],"learning.":[33],"performance":[35,218],"techniques":[38,142,180,221,239,293],"has":[39,52],"been":[40],"extensively":[41],"examined.":[42],"However,":[43],"date,":[45],"impact":[47,96,215,412],"sampling":[50,57,87,106,129,251,265,273,398],"strategies":[51],"received":[53],"limited":[54],"attention.":[55],"Random":[56],"widely":[59],"favored":[60],"because":[61],"its":[63],"ease":[64],"use.":[66],"It":[67,115],"treats":[68],"flood-related":[69],"as":[71,207],"tabular":[72],"excludes":[74],"their":[75],"dimensions.":[77],"Flood":[78,380],"occurrence":[79],"typically":[81],"uneven":[82],"over":[83],"space.":[84],"Therefore,":[85],"non-flood":[86,118],"should":[88],"not":[89],"be":[90,403],"completely":[91],"random.":[92],"To":[93],"represent":[94],"dimension,":[100],"this":[101,204],"study":[102,205],"proposed":[103,397],"a":[104,213,304],"new":[105,128,264],"approach":[107,130,266,399],"based":[108],"dependence,":[111],"called":[112],"inverse-occurrence":[113],"sampling.":[114],"selects":[116],"more":[117],"in":[120,124,143,147,222,288,299,308,312,315,320,324,331,335,365,386],"low-risk":[121],"areas":[122,146],"than":[123,219,260,314],"high-risk":[125],"areas.":[126],"was":[131,345],"compared":[132],"with":[133,150,376],"random":[134,172,186,317],"stratified":[136,328],"sampling,":[137],"using":[138,390],"six":[139],"machine":[140,184],"two":[144,272],"urban":[145],"Guangzhou,":[148],"China,":[149],"distinct":[151],"mechanisms,":[153],"that":[154,268],"is,":[155],"Tianhe":[156,309,321,332],"(flood":[157,169],"density":[158,170,375,381],"1.5/km2,":[159],"clustered":[160],"distribution,":[161,173],"average":[162,174,277],"slope":[163,175],"9.02\u00b0,":[164],"downtown":[165],"district)":[166],"Panyu":[168],"0.15/km2,":[171],"4.55\u00b0,":[176],"suburban":[177],"district).":[178],"Learning":[179],"include":[181],"support":[182],"vector":[183],"(SVM),":[185],"forest":[187],"(RF),":[188],"artificial":[189],"neural":[190,194],"networks":[191,195],"(ANNs),":[192],"convolutional":[193],"(CNNs),":[196],"CNN-SVM,":[197,350],"CNN-RF.":[199,393],"main":[201],"findings":[202],"were":[206,253],"follows:":[208],"(1)":[209],"Sampling":[210],"approaches":[211,252,274],"had":[212,303],"greater":[214],"terms":[223],"area":[225],"under":[226],"receiver":[228],"operating":[229],"characteristic":[230],"curve":[231],"(AUC).":[232],"AUC":[234,247,278,282,344],"variations":[235,248],"caused":[236,249],"by":[237,250,348],"ranged":[240],"from":[241],"0.04":[242],"0.09.":[244],"Meanwhile,":[245],"between":[254],"0.15":[255],"0.22,":[257],"all":[258],"larger":[259],"0.1.":[261],"(2)":[262],"outperformed":[267],"other":[271],"high":[276],"values":[279],"small":[281],"variations.":[283],"outperformance":[285],"robust":[287],"regard":[289],"multiple":[291],"different":[295],"flooding":[296],"mechanisms.":[297],"AUCs":[298],"inverse":[301],"group":[302,318,329],"narrower":[305],"range":[306],"(0.14\u20130.18":[307],"0.35\u20130.39":[311],"Panyu)":[313,325],"(0.24\u20130.28":[319],"0.43\u20130.53":[323],"(0.23\u20130.30":[330],"0.42\u20130.48":[334],"Panyu).":[336],"(3)":[337],"most":[339],"accurate":[340],"CNN-RF,":[346],"followed":[347],"SVM,":[349],"RF,":[351],"CNN,":[352],"ANN.":[354],"(4)":[355],"ANN-":[356],"CNN-based":[358],"models":[359,388],"tended":[360,383],"produce":[362],"polarized":[363],"patterns":[364],"maps,":[368],"contradicting":[369],"ascending":[371],"order":[372],"increasing":[377],"levels.":[379],"outliers":[382],"appear":[385],"derived":[389],"RF":[391],"Finally,":[394],"newly":[396],"suggested":[401],"applied":[404],"mapping":[408],"reflect":[410],"dependence.":[415]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}