{"id":"https://openalex.org/W4200545473","doi":"https://doi.org/10.3390/rs13234945","title":"Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets","display_name":"Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W4200545473","doi":"https://doi.org/10.3390/rs13234945"},"language":"en","primary_location":{"id":"doi:10.3390/rs13234945","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13234945","pdf_url":"https://www.mdpi.com/2072-4292/13/23/4945/pdf?version=1638766060","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/2072-4292/13/23/4945/pdf?version=1638766060","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043393568","display_name":"Jun Liu","orcid":"https://orcid.org/0000-0003-0435-0238"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Liu","raw_affiliation_strings":["School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"],"affiliations":[{"raw_affiliation_string":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103074762","display_name":"Jiyan Wang","orcid":"https://orcid.org/0000-0002-5336-6378"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiyan Wang","raw_affiliation_strings":["School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"],"affiliations":[{"raw_affiliation_string":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079763153","display_name":"Junnan Xiong","orcid":"https://orcid.org/0000-0001-6997-838X"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]},{"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/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junnan Xiong","raw_affiliation_strings":["Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China","School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China","institution_ids":["https://openalex.org/I4210160793","https://openalex.org/I19820366"]},{"raw_affiliation_string":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087300057","display_name":"Weiming Cheng","orcid":"https://orcid.org/0000-0003-1580-4979"},"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/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiming Cheng","raw_affiliation_strings":["Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China","institution_ids":["https://openalex.org/I4210160793","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036936200","display_name":"Huaizhang Sun","orcid":"https://orcid.org/0000-0002-0814-5488"},"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":"Huaizhang Sun","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010183940","display_name":"Zhiwei Yong","orcid":"https://orcid.org/0000-0003-0000-6659"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwei Yong","raw_affiliation_strings":["School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China"],"affiliations":[{"raw_affiliation_string":"School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100715682","display_name":"Nan Wang","orcid":"https://orcid.org/0000-0002-8321-7074"},"institutions":[{"id":"https://openalex.org/I184983240","display_name":"Northeast Normal University","ror":"https://ror.org/02rkvz144","country_code":"CN","type":"education","lineage":["https://openalex.org/I184983240"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Wang","raw_affiliation_strings":["School of Geographical Sciences, Northeast Normal University, Changchun 130024, China"],"affiliations":[{"raw_affiliation_string":"School of Geographical Sciences, Northeast Normal University, Changchun 130024, China","institution_ids":["https://openalex.org/I184983240"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5079763153"],"corresponding_institution_ids":["https://openalex.org/I165745306","https://openalex.org/I19820366","https://openalex.org/I4210160793"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":5.1881,"has_fulltext":false,"cited_by_count":77,"citation_normalized_percentile":{"value":0.96187113,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"13","issue":"23","first_page":"4945","last_page":"4945"},"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/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9962999820709229,"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"}},{"id":"https://openalex.org/T11186","display_name":"Hydrology and Drought Analysis","score":0.991599977016449,"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/support-vector-machine","display_name":"Support vector machine","score":0.706336259841919},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6527731418609619},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6398881673812866},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.6283550262451172},{"id":"https://openalex.org/keywords/bivariate-analysis","display_name":"Bivariate analysis","score":0.6161852478981018},{"id":"https://openalex.org/keywords/flash-flood","display_name":"Flash flood","score":0.5823305249214172},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5785953998565674},{"id":"https://openalex.org/keywords/bivariate-data","display_name":"Bivariate data","score":0.5423312187194824},{"id":"https://openalex.org/keywords/cart","display_name":"Cart","score":0.41585880517959595},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4143245816230774},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4122184216976166},{"id":"https://openalex.org/keywords/flood-myth","display_name":"Flood myth","score":0.31932783126831055},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07987412810325623}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.706336259841919},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6527731418609619},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6398881673812866},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.6283550262451172},{"id":"https://openalex.org/C64341305","wikidata":"https://www.wikidata.org/wiki/Q4919225","display_name":"Bivariate analysis","level":2,"score":0.6161852478981018},{"id":"https://openalex.org/C120417685","wikidata":"https://www.wikidata.org/wiki/Q860333","display_name":"Flash flood","level":3,"score":0.5823305249214172},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5785953998565674},{"id":"https://openalex.org/C148264743","wikidata":"https://www.wikidata.org/wiki/Q4919224","display_name":"Bivariate data","level":3,"score":0.5423312187194824},{"id":"https://openalex.org/C2777275308","wikidata":"https://www.wikidata.org/wiki/Q234668","display_name":"Cart","level":2,"score":0.41585880517959595},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4143245816230774},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4122184216976166},{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.31932783126831055},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07987412810325623},{"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/rs13234945","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13234945","pdf_url":"https://www.mdpi.com/2072-4292/13/23/4945/pdf?version=1638766060","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:f1d607f7a3b9441ca3203a36ab5e1c5b","is_oa":true,"landing_page_url":"https://doaj.org/article/f1d607f7a3b9441ca3203a36ab5e1c5b","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 13, Iss 23, p 4945 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/13/23/4945/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs13234945","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 13; Issue 23; Pages: 4945","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs13234945","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13234945","pdf_url":"https://www.mdpi.com/2072-4292/13/23/4945/pdf?version=1638766060","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4200545473.pdf","grobid_xml":"https://content.openalex.org/works/W4200545473.grobid-xml"},"referenced_works_count":79,"referenced_works":["https://openalex.org/W1483933478","https://openalex.org/W1584236903","https://openalex.org/W1983676031","https://openalex.org/W1993934953","https://openalex.org/W2026000237","https://openalex.org/W2027386095","https://openalex.org/W2041658927","https://openalex.org/W2042315239","https://openalex.org/W2065642067","https://openalex.org/W2066848039","https://openalex.org/W2079792879","https://openalex.org/W2085373553","https://openalex.org/W2106619162","https://openalex.org/W2116639981","https://openalex.org/W2120683298","https://openalex.org/W2122588877","https://openalex.org/W2126356715","https://openalex.org/W2199272311","https://openalex.org/W2478414316","https://openalex.org/W2598133371","https://openalex.org/W2601707113","https://openalex.org/W2605269605","https://openalex.org/W2606580569","https://openalex.org/W2612269362","https://openalex.org/W2640557513","https://openalex.org/W2735818415","https://openalex.org/W2748277187","https://openalex.org/W2761698665","https://openalex.org/W2763383283","https://openalex.org/W2766228856","https://openalex.org/W2771024073","https://openalex.org/W2777368278","https://openalex.org/W2780363565","https://openalex.org/W2789555074","https://openalex.org/W2791328889","https://openalex.org/W2791665776","https://openalex.org/W2796299618","https://openalex.org/W2802893388","https://openalex.org/W2802958163","https://openalex.org/W2895196240","https://openalex.org/W2899026392","https://openalex.org/W2901113940","https://openalex.org/W2904904697","https://openalex.org/W2911424673","https://openalex.org/W2912281062","https://openalex.org/W2916579675","https://openalex.org/W2918437719","https://openalex.org/W2927539500","https://openalex.org/W2939727542","https://openalex.org/W2946124949","https://openalex.org/W2958711105","https://openalex.org/W2979804492","https://openalex.org/W2989700724","https://openalex.org/W2996701347","https://openalex.org/W2998999740","https://openalex.org/W3006114317","https://openalex.org/W3019758389","https://openalex.org/W3032433182","https://openalex.org/W3036595569","https://openalex.org/W3038030066","https://openalex.org/W3048827138","https://openalex.org/W3050424506","https://openalex.org/W3076896779","https://openalex.org/W3087236291","https://openalex.org/W3091821738","https://openalex.org/W3093835305","https://openalex.org/W3108031584","https://openalex.org/W3119067073","https://openalex.org/W3130081654","https://openalex.org/W3131138274","https://openalex.org/W3135147416","https://openalex.org/W3138479519","https://openalex.org/W3140276143","https://openalex.org/W3141421476","https://openalex.org/W4206686222","https://openalex.org/W6670559801","https://openalex.org/W6683581212","https://openalex.org/W6721347384","https://openalex.org/W6869666550"],"related_works":["https://openalex.org/W4220854656","https://openalex.org/W4232399475","https://openalex.org/W2474319518","https://openalex.org/W89977309","https://openalex.org/W1575790908","https://openalex.org/W3122841410","https://openalex.org/W4447228","https://openalex.org/W2901858382","https://openalex.org/W2150332776","https://openalex.org/W2060480178"],"abstract_inverted_index":{"Flash":[0],"floods":[1],"are":[2,15],"considered":[3],"to":[4,17,100,125],"be":[5],"one":[6],"of":[7,51,62,129,164,182,214,251,260],"the":[8,38,52,96,104,109,127,130,135,142,145,150,157,160,165,169,180,183,211,215,249,252,261],"most":[9],"destructive":[10],"natural":[11],"hazards,":[12],"and":[13,20,31,57,68,72,91,102,120,153,156,168,174,218,223,232,235,242,258,268],"they":[14],"difficult":[16],"accurately":[18],"model":[19,148,185],"predict.":[21],"In":[22],"this":[23,279],"study,":[24,280],"three":[25,43,58,105,184,190,274],"hybrid":[26,44,106,147,191,275],"models":[27,45,192,276],"were":[28,123,172,220,229,239],"proposed,":[29],"evaluated,":[30],"used":[32,99,124],"for":[33,287],"flood":[34,85,89,253,271,290],"susceptibility":[35,254,291],"prediction":[36,154,170,195],"in":[37,278,289,293,296],"Dadu":[39],"River":[40],"Basin.":[41],"These":[42],"integrate":[46],"a":[47,78,284],"bivariate":[48],"statistical":[49],"method":[50],"fuzzy":[53],"membership":[54],"value":[55],"(FMV)":[56],"machine":[59,65,201,208],"learning":[60,202,209],"methods":[61],"support":[63],"vector":[64],"(SVM),":[66],"classification":[67,121,237],"regression":[69],"trees":[70],"(CART),":[71],"convolutional":[73],"neural":[74],"network":[75],"(CNN).":[76],"Firstly,":[77],"geospatial":[79],"database":[80,97],"was":[81,98,264],"prepared":[82],"comprising":[83],"nine":[84],"conditioning":[86],"factors,":[87],"485":[88,92],"locations,":[90],"non-flood":[93],"locations.":[94],"Then,":[95],"train":[101],"test":[103],"models.":[107,131,203],"Subsequently,":[108],"receiver":[110],"operating":[111],"characteristic":[112],"(ROC)":[113],"curve,":[114],"seed":[115],"cell":[116],"area":[117,158,263],"index":[118],"(SCAI),":[119],"accuracy":[122],"evaluate":[126],"performances":[128],"The":[132,138,273],"results":[133,181,250],"reveal":[134],"following:":[136],"(1)":[137],"ROC":[139],"curve":[140,161],"highlights":[141],"fact":[143],"that":[144],"CNN-FMV":[146,219],"had":[149,193],"best":[151],"fitting":[152],"performance,":[155],"under":[159],"(AUC)":[162],"values":[163,213,228],"success":[166],"rate":[167,171],"0.935":[173],"0.912,":[175],"respectively.":[176,245],"(2)":[177],"Based":[178,247],"on":[179,248],"performance":[186],"evaluation":[187],"methods,":[188],"all":[189],"better":[194],"capabilities":[196],"than":[197],"their":[198,206,226,236],"respective":[199],"single":[200,207],"Compared":[204],"with":[205],"models,":[210],"AUC":[212],"SVM-FMV,":[216],"CART-FMV,":[217],"0.032,":[221],"0.005,":[222],"0.055":[224],"higher;":[225],"SCAI":[227],"0.05,":[230],"0.03,":[231],"0.02":[233],"lower;":[234],"accuracies":[238],"4.48%,":[240],"1.38%,":[241],"5.86%":[243],"higher,":[244],"(3)":[246],"indices,":[255],"between":[256],"13.21%":[257],"22.03%":[259],"study":[262],"characterized":[265],"by":[266],"high":[267,270,285],"very":[269],"susceptibilities.":[272],"proposed":[277],"especially":[281],"CNN-FMV,":[282],"have":[283],"potential":[286],"application":[288],"assessment":[292],"specific":[294],"areas":[295],"future":[297],"studies.":[298]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":22}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2021-12-31T00:00:00"}
