{"id":"https://openalex.org/W4413129562","doi":"https://doi.org/10.3389/fams.2025.1653562","title":"Enhancing disaster prediction with Bayesian deep learning: a robust approach for uncertainty estimation","display_name":"Enhancing disaster prediction with Bayesian deep learning: a robust approach for uncertainty estimation","publication_year":2025,"publication_date":"2025-08-12","ids":{"openalex":"https://openalex.org/W4413129562","doi":"https://doi.org/10.3389/fams.2025.1653562"},"language":"en","primary_location":{"id":"doi:10.3389/fams.2025.1653562","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fams.2025.1653562","pdf_url":"https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1653562/pdf","source":{"id":"https://openalex.org/S2597085352","display_name":"Frontiers in Applied Mathematics and Statistics","issn_l":"2297-4687","issn":["2297-4687"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Applied Mathematics and Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1653562/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100740618","display_name":"Hao Peng","orcid":"https://orcid.org/0000-0001-7422-630X"},"institutions":[{"id":"https://openalex.org/I4210145500","display_name":"Guizhou Electric Power Design and Research Institute","ror":"https://ror.org/055f13495","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210145500"]},{"id":"https://openalex.org/I4210120238","display_name":"PowerChina (China)","ror":"https://ror.org/01varr368","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210120238"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Peng","raw_affiliation_strings":["Power China Guiyang Engineering Corporation Limited, Guiyang, China"],"affiliations":[{"raw_affiliation_string":"Power China Guiyang Engineering Corporation Limited, Guiyang, China","institution_ids":["https://openalex.org/I4210145500","https://openalex.org/I4210120238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111875641","display_name":"Sen Shen","orcid":"https://orcid.org/0009-0002-1940-9781"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Shen","raw_affiliation_strings":["Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101758341","display_name":"Haichao Zhang","orcid":"https://orcid.org/0000-0003-0154-2947"},"institutions":[{"id":"https://openalex.org/I4210120238","display_name":"PowerChina (China)","ror":"https://ror.org/01varr368","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210120238"]},{"id":"https://openalex.org/I4210145500","display_name":"Guizhou Electric Power Design and Research Institute","ror":"https://ror.org/055f13495","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210145500"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haichao Zhang","raw_affiliation_strings":["Power China Guiyang Engineering Corporation Limited, Guiyang, China"],"affiliations":[{"raw_affiliation_string":"Power China Guiyang Engineering Corporation Limited, Guiyang, China","institution_ids":["https://openalex.org/I4210145500","https://openalex.org/I4210120238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455817","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0002-3282-0535"},"institutions":[{"id":"https://openalex.org/I4210145500","display_name":"Guizhou Electric Power Design and Research Institute","ror":"https://ror.org/055f13495","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210145500"]},{"id":"https://openalex.org/I4210120238","display_name":"PowerChina (China)","ror":"https://ror.org/01varr368","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210120238"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Power China Guiyang Engineering Corporation Limited, Guiyang, China"],"affiliations":[{"raw_affiliation_string":"Power China Guiyang Engineering Corporation Limited, Guiyang, China","institution_ids":["https://openalex.org/I4210145500","https://openalex.org/I4210120238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026440004","display_name":"Fawang Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145500","display_name":"Guizhou Electric Power Design and Research Institute","ror":"https://ror.org/055f13495","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210145500"]},{"id":"https://openalex.org/I4210120238","display_name":"PowerChina (China)","ror":"https://ror.org/01varr368","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210120238"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fawang Guo","raw_affiliation_strings":["Power China Guiyang Engineering Corporation Limited, Guiyang, China"],"affiliations":[{"raw_affiliation_string":"Power China Guiyang Engineering Corporation Limited, Guiyang, China","institution_ids":["https://openalex.org/I4210145500","https://openalex.org/I4210120238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005161470","display_name":"Ruige Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruige Zhang","raw_affiliation_strings":["Henan Zhuqueyun Network Technology Co., Ltd., Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"Henan Zhuqueyun Network Technology Co., Ltd., Zhengzhou, China","institution_ids":["https://openalex.org/I6507939"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100740618"],"corresponding_institution_ids":["https://openalex.org/I4210120238","https://openalex.org/I4210145500"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":1.1543,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.80685616,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"11","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9998999834060669,"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":0.9998999834060669,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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":0.9959999918937683,"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/computer-science","display_name":"Computer science","score":0.7054793238639832},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.6320446729660034},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5628756284713745},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.523614227771759},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5205572247505188},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49949169158935547},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4745817184448242},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.46967750787734985},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4554654061794281},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.45110005140304565},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.441104918718338},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4405660927295685},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4387591481208801},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42249977588653564}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7054793238639832},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.6320446729660034},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5628756284713745},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.523614227771759},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5205572247505188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49949169158935547},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4745817184448242},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46967750787734985},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4554654061794281},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.45110005140304565},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.441104918718338},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4405660927295685},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4387591481208801},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42249977588653564},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3389/fams.2025.1653562","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fams.2025.1653562","pdf_url":"https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1653562/pdf","source":{"id":"https://openalex.org/S2597085352","display_name":"Frontiers in Applied Mathematics and Statistics","issn_l":"2297-4687","issn":["2297-4687"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Applied Mathematics and Statistics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:40888a33ea91470699f46e9dd758dd6b","is_oa":true,"landing_page_url":"https://doaj.org/article/40888a33ea91470699f46e9dd758dd6b","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":"Frontiers in Applied Mathematics and Statistics, Vol 11 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fams.2025.1653562","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fams.2025.1653562","pdf_url":"https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1653562/pdf","source":{"id":"https://openalex.org/S2597085352","display_name":"Frontiers in Applied Mathematics and Statistics","issn_l":"2297-4687","issn":["2297-4687"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Applied Mathematics and Statistics","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Climate action","score":0.8299999833106995,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413129562.pdf"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W2123830299","https://openalex.org/W2280712870","https://openalex.org/W2893556584","https://openalex.org/W2899023746","https://openalex.org/W2902913073","https://openalex.org/W2905485021","https://openalex.org/W2920820407","https://openalex.org/W2977009307","https://openalex.org/W2991218926","https://openalex.org/W2993714058","https://openalex.org/W2995368577","https://openalex.org/W2997861936","https://openalex.org/W3037111239","https://openalex.org/W3044320885","https://openalex.org/W3091948139","https://openalex.org/W3100824565","https://openalex.org/W3102100346","https://openalex.org/W3107240659","https://openalex.org/W3134694888","https://openalex.org/W3137664849","https://openalex.org/W3140841781","https://openalex.org/W3164556435","https://openalex.org/W3164731060","https://openalex.org/W3172399786","https://openalex.org/W4200366988","https://openalex.org/W4229369233","https://openalex.org/W4233043189","https://openalex.org/W4281746598","https://openalex.org/W4283080878","https://openalex.org/W4285124156","https://openalex.org/W4301400955","https://openalex.org/W4382601168","https://openalex.org/W4383227059","https://openalex.org/W4402407201","https://openalex.org/W4402603168","https://openalex.org/W4404142184","https://openalex.org/W4406692827","https://openalex.org/W4408294368","https://openalex.org/W4408410965","https://openalex.org/W4409218903","https://openalex.org/W4409569372","https://openalex.org/W4409897881","https://openalex.org/W4410234308","https://openalex.org/W4410369194","https://openalex.org/W6790916381","https://openalex.org/W6854597063","https://openalex.org/W6908317183"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W4412335227","https://openalex.org/W2185578297","https://openalex.org/W2782041652","https://openalex.org/W2612657834","https://openalex.org/W2392157706","https://openalex.org/W2599192953","https://openalex.org/W1987310671","https://openalex.org/W2952088488","https://openalex.org/W1521968289"],"abstract_inverted_index":{"Accurate":[0],"disaster":[1,152],"prediction":[2,86,140],"combined":[3],"with":[4],"reliable":[5],"uncertainty":[6,25,96],"quantification":[7],"is":[8],"crucial":[9],"for":[10,50],"timely":[11],"and":[12,62,70,114,135,154],"effective":[13],"decision-making":[14],"in":[15,32,129,161],"emergency":[16,158],"management.":[17],"However,":[18],"traditional":[19],"deep":[20],"learning":[21],"methods":[22],"generally":[23],"lack":[24],"estimation":[26],"capabilities,":[27],"limiting":[28],"their":[29],"practical":[30],"effectiveness":[31],"high-risk":[33],"scenarios.":[34],"To":[35],"overcome":[36],"these":[37],"limitations,":[38],"this":[39],"study":[40],"proposes":[41],"an":[42],"enhanced":[43],"Bayesian":[44],"Deep":[45],"Neural":[46],"Network":[47],"(BDNN)":[48],"tailored":[49],"flood":[51],"forecasting,":[52],"effectively":[53],"integrating":[54],"Variational":[55],"Inference":[56],"(VI),":[57],"Monte":[58],"Carlo":[59],"(MC)":[60],"Dropout,":[61],"a":[63],"Hierarchical":[64],"Attention":[65],"Mechanism.":[66],"By":[67],"leveraging":[68],"hydrological":[69],"meteorological":[71],"data":[72],"from":[73],"the":[74,79,102,121,125,145],"Yellow":[75],"River":[76],"basin":[77],"(2001\u20132023),":[78],"BDNN":[80],"model":[81],"not":[82],"only":[83],"achieves":[84],"superior":[85],"accuracy":[87],"(94.6%)":[88],"but":[89],"also":[90],"significantly":[91,150],"enhances":[92],"reliability":[93],"through":[94],"robust":[95],"quantification.":[97],"Comparative":[98],"analyses":[99],"demonstrate":[100],"that":[101],"proposed":[103],"approach":[104],"markedly":[105],"outperforms":[106],"conventional":[107],"models":[108],"such":[109],"as":[110],"Random":[111],"Forest,":[112],"XGBoost,":[113],"Multi-layer":[115],"Perceptron.":[116],"Ablation":[117],"studies":[118],"further":[119],"confirm":[120],"critical":[122],"role":[123],"of":[124,147],"hierarchical":[126],"attention":[127],"mechanism":[128],"capturing":[130],"essential":[131],"features,":[132],"while":[133],"VI":[134],"MC":[136],"Dropout":[137],"substantially":[138],"improve":[139],"reliability.":[141],"These":[142],"advancements":[143],"highlight":[144],"potential":[146],"BDNNs":[148],"to":[149],"enhance":[151],"preparedness":[153],"support":[155],"more":[156],"informed":[157],"response":[159],"decisions":[160],"complex,":[162],"uncertain":[163],"environments.":[164]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}
