{"id":"https://openalex.org/W4384822293","doi":"https://doi.org/10.3390/rs15143601","title":"Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data","display_name":"Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data","publication_year":2023,"publication_date":"2023-07-19","ids":{"openalex":"https://openalex.org/W4384822293","doi":"https://doi.org/10.3390/rs15143601"},"language":"en","primary_location":{"id":"doi:10.3390/rs15143601","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15143601","pdf_url":"https://www.mdpi.com/2072-4292/15/14/3601/pdf?version=1689756499","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/2072-4292/15/14/3601/pdf?version=1689756499","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101452989","display_name":"Han Yu","orcid":"https://orcid.org/0000-0002-9047-5362"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han Yu","raw_affiliation_strings":["Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China","State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059061798","display_name":"Zengliang Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zengliang Luo","raw_affiliation_strings":["Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China","State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083980544","display_name":"Lunche Wang","orcid":"https://orcid.org/0000-0001-7783-5725"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lunche Wang","raw_affiliation_strings":["Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China","State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102092334","display_name":"Xiangyi Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092032","display_name":"China Institute of Water Resources and Hydropower Research","ror":"https://ror.org/00m4czf33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210092032"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyi Ding","raw_affiliation_strings":["Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China"],"affiliations":[{"raw_affiliation_string":"Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China","institution_ids":["https://openalex.org/I4210092032"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025438179","display_name":"Shaoqiang Wang","orcid":"https://orcid.org/0000-0002-4111-6689"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoqiang Wang","raw_affiliation_strings":["Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China","State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5059061798"],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":4.9678,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.96084487,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"15","issue":"14","first_page":"3601","last_page":"3601"},"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/T11483","display_name":"Tropical and Extratropical Cyclones Research","score":0.9947999715805054,"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/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"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/machine-learning","display_name":"Machine learning","score":0.7402133941650391},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7114728093147278},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7060199975967407},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6789261698722839},{"id":"https://openalex.org/keywords/flood-myth","display_name":"Flood myth","score":0.6715376377105713},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.632323145866394},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6011664867401123},{"id":"https://openalex.org/keywords/flood-forecasting","display_name":"Flood forecasting","score":0.502490758895874},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.48768460750579834},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.46451109647750854},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.45684540271759033},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.45175328850746155},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42072394490242004},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1256929337978363},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07987600564956665}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7402133941650391},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7114728093147278},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7060199975967407},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6789261698722839},{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.6715376377105713},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.632323145866394},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6011664867401123},{"id":"https://openalex.org/C183195422","wikidata":"https://www.wikidata.org/wiki/Q3409303","display_name":"Flood forecasting","level":3,"score":0.502490758895874},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.48768460750579834},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.46451109647750854},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.45684540271759033},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.45175328850746155},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42072394490242004},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1256929337978363},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07987600564956665},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs15143601","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15143601","pdf_url":"https://www.mdpi.com/2072-4292/15/14/3601/pdf?version=1689756499","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:cee578538220471d9bad8df869860125","is_oa":true,"landing_page_url":"https://doaj.org/article/cee578538220471d9bad8df869860125","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 14, p 3601 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/14/3601/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15143601","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; Volume 15; Issue 14; Pages: 3601","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15143601","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15143601","pdf_url":"https://www.mdpi.com/2072-4292/15/14/3601/pdf?version=1689756499","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","display_name":"Sustainable cities and communities","score":0.7099999785423279}],"awards":[{"id":"https://openalex.org/G3579746021","display_name":null,"funder_award_id":"42201038","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":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4384822293.pdf"},"referenced_works_count":60,"referenced_works":["https://openalex.org/W33331100","https://openalex.org/W1975914988","https://openalex.org/W1998025025","https://openalex.org/W1999251352","https://openalex.org/W2024168391","https://openalex.org/W2042315239","https://openalex.org/W2063987149","https://openalex.org/W2071534821","https://openalex.org/W2133059825","https://openalex.org/W2142827986","https://openalex.org/W2302012322","https://openalex.org/W2487770199","https://openalex.org/W2640557513","https://openalex.org/W2765742909","https://openalex.org/W2884764722","https://openalex.org/W2895196240","https://openalex.org/W2904486608","https://openalex.org/W2910531141","https://openalex.org/W2946020082","https://openalex.org/W2947721686","https://openalex.org/W2955858817","https://openalex.org/W2956021635","https://openalex.org/W2989920058","https://openalex.org/W2990338582","https://openalex.org/W2996701347","https://openalex.org/W2998917588","https://openalex.org/W3003923526","https://openalex.org/W3021282064","https://openalex.org/W3033145000","https://openalex.org/W3038030066","https://openalex.org/W3038187968","https://openalex.org/W3081610994","https://openalex.org/W3091948139","https://openalex.org/W3096207306","https://openalex.org/W3096857981","https://openalex.org/W3103865021","https://openalex.org/W3128567141","https://openalex.org/W3131003930","https://openalex.org/W3170833241","https://openalex.org/W3176462777","https://openalex.org/W3183485891","https://openalex.org/W3189521957","https://openalex.org/W3212177475","https://openalex.org/W3215138361","https://openalex.org/W4214730718","https://openalex.org/W4220688375","https://openalex.org/W4220934285","https://openalex.org/W4221036186","https://openalex.org/W4224991974","https://openalex.org/W4226536463","https://openalex.org/W4283016790","https://openalex.org/W4285009884","https://openalex.org/W4289531761","https://openalex.org/W4290830341","https://openalex.org/W4293577718","https://openalex.org/W4297231277","https://openalex.org/W4304140007","https://openalex.org/W4319324434","https://openalex.org/W4367298578","https://openalex.org/W6776569670"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W4386690025","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856"],"abstract_inverted_index":{"Sufficient":[0],"historical":[1,19],"flood":[2,11,49,67,140,167,249,259,266],"inventory":[3],"data":[4],"(FID)":[5],"are":[6,21],"crucial":[7],"for":[8,62,148,165,258],"accurately":[9],"predicting":[10,166],"susceptibility":[12,50,68,168,250,260],"using":[13,80],"supervised":[14],"machine":[15,73,87,123,151,162,196],"learning":[16,74,88,152,163,197],"models.":[17,130,153],"However,":[18,36],"FID":[20,39,79,147],"insufficient":[22],"in":[23,220,236,262],"many":[24],"regions.":[25],"Remote":[26],"sensing":[27,43],"provides":[28,254],"a":[29,58,105,247],"promising":[30],"opportunity":[31],"to":[32,92,144,224],"expand":[33,145],"the":[34,38,46,64,77,94,97,109,146,150,158,161,174,184,187,194,203,206,209,213,216,225,228,234],"FID.":[35],"whether":[37],"expanded":[40,78,175],"by":[41,71,100,172,179],"remote":[42],"can":[44],"improve":[45],"accuracy":[47,65,95],"of":[48,66,96,160,227,233],"modeling":[51],"needs":[52],"further":[53],"study.":[54],"In":[55],"this":[56],"study,":[57,107],"framework":[59],"was":[60,169],"proposed":[61,98],"improving":[63],"prediction":[69],"(FSP)":[70],"combining":[72],"models":[75,89,164],"and":[76,125,138,243],"Sentinel-1A":[81,131],"radar":[82],"images.":[83],"Five":[84],"widely":[85],"used":[86,143,195],"were":[90,142],"employed":[91],"verify":[93],"method":[99],"taking":[101,199],"Wuhan":[102,221,237],"City":[103],"as":[104],"case":[106],"including":[108],"random":[110],"forest":[111],"(RF),":[112],"gradient":[113],"boosting":[114],"decision":[115],"tree":[116],"(GBDT),":[117],"k-nearest":[118],"neighbor":[119],"(KNN),":[120],"support":[121],"vector":[122],"(SVM),":[124],"artificial":[126],"neural":[127],"network":[128],"(ANN)":[129],"images":[132],"from":[133],"time":[134],"points":[135],"before,":[136],"during,":[137],"after":[139],"events":[141],"training":[149],"The":[154],"results":[155,226],"showed":[156,208,215],"that":[157],"performance":[159,219],"improved":[170,178],"greatly":[171],"considering":[173],"FID,":[176],"being":[177],"approximately":[180,231],"1.14\u201319.74%":[181],"based":[182],"on":[183],"area":[185,235],"under":[186],"receiver":[188],"operating":[189],"characteristic":[190],"curve":[191],"(AUC).":[192],"Among":[193],"models,":[198],"into":[200],"account":[201],"all":[202],"statistical":[204],"indicators,":[205],"ANN":[207,229],"best":[210,217],"performance,":[211],"while":[212],"SVM":[214],"generalization":[218],"City.":[222],"According":[223],"model,":[230],"19%":[232],"City,":[238],"mainly":[239],"distributed":[240],"near":[241],"rivers":[242],"lakes,":[244],"is":[245],"at":[246],"high":[248],"level.":[251],"This":[252],"study":[253],"an":[255],"essential":[256],"reference":[257],"analyses":[261],"regions":[263],"with":[264],"limited":[265],"sampling":[267],"data.":[268]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":11}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2023-07-21T00:00:00"}