{"id":"https://openalex.org/W4385161938","doi":"https://doi.org/10.3390/rs15143678","title":"Urban Flood Risk Assessment through the Integration of Natural and Human Resilience Based on Machine Learning Models","display_name":"Urban Flood Risk Assessment through the Integration of Natural and Human Resilience Based on Machine Learning Models","publication_year":2023,"publication_date":"2023-07-23","ids":{"openalex":"https://openalex.org/W4385161938","doi":"https://doi.org/10.3390/rs15143678"},"language":"en","primary_location":{"id":"doi:10.3390/rs15143678","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15143678","pdf_url":"https://www.mdpi.com/2072-4292/15/14/3678/pdf?version=1690172287","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/2072-4292/15/14/3678/pdf?version=1690172287","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100350553","display_name":"Wenting Zhang","orcid":"https://orcid.org/0000-0003-2236-7148"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]},{"id":"https://openalex.org/I4210099662","display_name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","ror":"https://ror.org/012wsxz85","country_code":"CN","type":"facility","lineage":["https://openalex.org/I163340411","https://openalex.org/I4210099662","https://openalex.org/I4210111986","https://openalex.org/I4210120069","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenting Zhang","raw_affiliation_strings":["College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China","The State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"The State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China","institution_ids":["https://openalex.org/I4210099662","https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100380066","display_name":"Bin Hu","orcid":"https://orcid.org/0000-0003-3514-5413"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Hu","raw_affiliation_strings":["College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101857227","display_name":"Yongzhi Liu","orcid":"https://orcid.org/0000-0002-4181-9851"},"institutions":[{"id":"https://openalex.org/I4210099662","display_name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","ror":"https://ror.org/012wsxz85","country_code":"CN","type":"facility","lineage":["https://openalex.org/I163340411","https://openalex.org/I4210099662","https://openalex.org/I4210111986","https://openalex.org/I4210120069","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210111986","display_name":"Nanjing Hydraulic Research Institute","ror":"https://ror.org/02403qw73","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210111986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongzhi Liu","raw_affiliation_strings":["Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China","The State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, NHRI, Nanjing 210029, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China","institution_ids":["https://openalex.org/I4210111986"]},{"raw_affiliation_string":"The State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, NHRI, Nanjing 210029, China","institution_ids":["https://openalex.org/I4210099662"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076492280","display_name":"Xingnan Zhang","orcid":"https://orcid.org/0000-0001-8537-1529"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingnan Zhang","raw_affiliation_strings":["College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020817752","display_name":"Zhixuan Li","orcid":"https://orcid.org/0000-0001-7522-6678"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhixuan Li","raw_affiliation_strings":["College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China","institution_ids":["https://openalex.org/I163340411"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100350553"],"corresponding_institution_ids":["https://openalex.org/I163340411","https://openalex.org/I4210099662"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":6.3876,"has_fulltext":true,"cited_by_count":44,"citation_normalized_percentile":{"value":0.97454545,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"15","issue":"14","first_page":"3678","last_page":"3678"},"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":0.9944000244140625,"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":0.9923999905586243,"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":0.6374621987342834},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5951879024505615},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5800554156303406},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5428142547607422},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49721410870552063},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4905042052268982},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.45247402787208557},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4510374665260315},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.44985660910606384},{"id":"https://openalex.org/keywords/community-resilience","display_name":"Community resilience","score":0.4257264733314514},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.26247209310531616},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.2286117970943451}],"concepts":[{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.6374621987342834},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5951879024505615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5800554156303406},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5428142547607422},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49721410870552063},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4905042052268982},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.45247402787208557},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4510374665260315},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.44985660910606384},{"id":"https://openalex.org/C2779488668","wikidata":"https://www.wikidata.org/wiki/Q3457767","display_name":"Community resilience","level":3,"score":0.4257264733314514},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26247209310531616},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2286117970943451},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs15143678","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15143678","pdf_url":"https://www.mdpi.com/2072-4292/15/14/3678/pdf?version=1690172287","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:7ce603beb61441e5ab6ce6e02d6bd24f","is_oa":true,"landing_page_url":"https://doaj.org/article/7ce603beb61441e5ab6ce6e02d6bd24f","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":"Remote Sensing, Vol 15, Iss 14, p 3678 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/14/3678/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15143678","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: 3678","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15143678","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15143678","pdf_url":"https://www.mdpi.com/2072-4292/15/14/3678/pdf?version=1690172287","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":0.4300000071525574,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G1227247924","display_name":null,"funder_award_id":"92047203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2438743225","display_name":null,"funder_award_id":"42175177, U2240216","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G250588315","display_name":null,"funder_award_id":"2019YFC1510204","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G2732121128","display_name":null,"funder_award_id":"42175177","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4515127127","display_name":null,"funder_award_id":"42075191","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7963650187","display_name":null,"funder_award_id":"U2240216","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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4385161938.pdf"},"referenced_works_count":64,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W588320544","https://openalex.org/W1498436455","https://openalex.org/W1678356000","https://openalex.org/W1974614011","https://openalex.org/W1989810169","https://openalex.org/W2001345576","https://openalex.org/W2007802759","https://openalex.org/W2009290038","https://openalex.org/W2012930983","https://openalex.org/W2038188901","https://openalex.org/W2042315239","https://openalex.org/W2070606289","https://openalex.org/W2071968219","https://openalex.org/W2092556113","https://openalex.org/W2122588877","https://openalex.org/W2147406638","https://openalex.org/W2149298154","https://openalex.org/W2176478590","https://openalex.org/W2355063754","https://openalex.org/W2367568479","https://openalex.org/W2392263831","https://openalex.org/W2516880899","https://openalex.org/W2594352094","https://openalex.org/W2606804832","https://openalex.org/W2732516007","https://openalex.org/W2745132597","https://openalex.org/W2811032661","https://openalex.org/W2895196240","https://openalex.org/W2902923351","https://openalex.org/W2904486608","https://openalex.org/W2941121100","https://openalex.org/W2973053290","https://openalex.org/W2996701347","https://openalex.org/W3005791898","https://openalex.org/W3006494367","https://openalex.org/W3008924545","https://openalex.org/W3023118770","https://openalex.org/W3035968653","https://openalex.org/W3036595569","https://openalex.org/W3038030066","https://openalex.org/W3109983083","https://openalex.org/W3116724951","https://openalex.org/W3131138274","https://openalex.org/W3140276143","https://openalex.org/W3164556435","https://openalex.org/W3176511636","https://openalex.org/W3197367277","https://openalex.org/W3198066336","https://openalex.org/W3204751889","https://openalex.org/W3214840804","https://openalex.org/W4206955329","https://openalex.org/W4220712909","https://openalex.org/W4220946332","https://openalex.org/W4230777928","https://openalex.org/W4239510810","https://openalex.org/W4288757776","https://openalex.org/W4289767170","https://openalex.org/W4297200317","https://openalex.org/W4302424479","https://openalex.org/W4308116318","https://openalex.org/W4313471424","https://openalex.org/W4327694208","https://openalex.org/W6840865271"],"related_works":["https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4313289487","https://openalex.org/W4321636153","https://openalex.org/W2792755205","https://openalex.org/W4319461045","https://openalex.org/W4376104652","https://openalex.org/W2994772185","https://openalex.org/W143660212"],"abstract_inverted_index":{"Flood":[0],"risk":[1,250],"assessment":[2,24,251],"and":[3,33,38,48,59,80,90,169,252],"mapping":[4],"are":[5,163,176],"considered":[6],"essential":[7],"tools":[8],"for":[9,238],"the":[10,107,111,115,119,123,133,143,157,173,183,187,203,211,218,225,235,239,257],"improvement":[11],"of":[12,67,125,135,146,156,189,205,227,241,256,260],"flood":[13,23,99,249,266],"management.":[14],"This":[15,223,232],"research":[16],"aims":[17],"to":[18,30,154,207,245],"construct":[19],"a":[20,93],"more":[21],"comprehensive":[22],"framework":[25],"by":[26],"emphasizing":[27],"factors":[28],"related":[29],"human":[31,229,261],"resilience":[32,230,262],"integrating":[34],"them":[35],"with":[36,65,186],"meteorological":[37],"geographical":[39],"factors.":[40,231],"Moreover,":[41],"two":[42],"ensemble":[43,147],"learning":[44,70,148,243],"models,":[45,71],"namely":[46],"voting":[47],"stacking,":[49],"which":[50,161],"utilize":[51],"heterogeneous":[52],"learners,":[53],"were":[54,88],"employed":[55,247],"in":[56,101,179,193,248,263],"this":[57],"study,":[58],"their":[60],"prediction":[61,144],"performance":[62],"was":[63],"compared":[64],"that":[66,142,202],"traditional":[68],"machine":[69,242],"including":[72],"support":[73],"vector":[74],"machine,":[75],"random":[76],"forest,":[77],"multilayer":[78],"perceptron,":[79],"gradient":[81],"boosting":[82],"decision":[83],"tree.":[84],"The":[85,104,129,166],"six":[86],"models":[87,159],"trained":[89],"tested":[91],"using":[92],"sample":[94],"database":[95],"constructed":[96],"from":[97],"historical":[98,194],"events":[100],"Hefei,":[102],"China.":[103],"results":[105,145],"demonstrated":[106],"following":[108],"findings:":[109],"(1)":[110],"RF":[112],"model":[113,121,131],"exhibited":[114],"highest":[116],"accuracy,":[117],"while":[118],"SVR":[120],"underestimated":[122,132],"extent":[124,134],"extremely":[126],"high-risk":[127,168],"areas.":[128,137],"stacking":[130],"very-high-risk":[136,170],"It":[138,198],"should":[139],"be":[140,152,246],"noted":[141],"methods":[149,244],"may":[150],"not":[151],"superior":[153],"those":[155],"base":[158],"upon":[160],"they":[162],"built.":[164],"(2)":[165],"predicted":[167],"areas":[171,191],"within":[172],"study":[174,233],"area":[175],"predominantly":[177],"clustered":[178],"low-lying":[180],"regions":[181],"along":[182],"rivers,":[184],"aligning":[185],"distribution":[188],"hazardous":[190],"observed":[192],"inundation":[195],"events.":[196],"(3)":[197],"is":[199],"worth":[200],"noting":[201],"factor":[204],"distance":[206],"pumping":[208],"stations":[209],"has":[210],"second":[212],"most":[213],"significant":[214],"driving":[215],"influence":[216],"after":[217],"DEM":[219],"(Digital":[220],"Elevation":[221],"Model).":[222],"underscores":[224],"importance":[226],"considering":[228],"expands":[234],"empirical":[236],"evidence":[237],"ability":[240],"deepens":[253],"our":[254],"understanding":[255],"potential":[258],"mechanisms":[259],"influencing":[264],"urban":[265],"risk.":[267]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":11},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-16T09:24:06.705377","created_date":"2025-10-10T00:00:00"}