{"id":"https://openalex.org/W4413867513","doi":"https://doi.org/10.32604/cmc.2025.067639","title":"Short-Term Multi-Hazard Prediction Using a Multi-Source Data Fusion Approach","display_name":"Short-Term Multi-Hazard Prediction Using a Multi-Source Data Fusion Approach","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4413867513","doi":"https://doi.org/10.32604/cmc.2025.067639"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.067639","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.067639","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.067639","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108848855","display_name":"Syeda Zoupash Zahra","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Syeda Zoupash Zahra","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082033110","display_name":"Najia Saher","orcid":"https://orcid.org/0000-0003-0368-7496"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Najia Saher","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067730154","display_name":"Malik Muhammad Saad Missen","orcid":"https://orcid.org/0000-0001-9903-0274"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Malik Muhammad Saad Missen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016019901","display_name":"Rab Nawaz Bashir","orcid":"https://orcid.org/0000-0001-7409-1775"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rab Nawaz Bashir","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Salma Idris","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salma Idris","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041891989","display_name":"Tahani Jaser Alahmadi","orcid":"https://orcid.org/0000-0002-0067-692X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tahani Jaser Alahmadi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Muhammad Inshal Khan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muhammad Inshal Khan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5108848855"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2472,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85197287,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"85","issue":"3","first_page":"4869","last_page":"4883"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11357","display_name":"Risk and Safety Analysis","score":0.9587000012397766,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11357","display_name":"Risk and Safety Analysis","score":0.9587000012397766,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9585000276565552,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10809","display_name":"Occupational Health and Safety Research","score":0.9503999948501587,"subfield":{"id":"https://openalex.org/subfields/3614","display_name":"Radiological and Ultrasound Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.7458511590957642},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5364155769348145},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5126625895500183},{"id":"https://openalex.org/keywords/hazard","display_name":"Hazard","score":0.4521487057209015},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3830867409706116},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07150387763977051}],"concepts":[{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.7458511590957642},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5364155769348145},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5126625895500183},{"id":"https://openalex.org/C49261128","wikidata":"https://www.wikidata.org/wiki/Q1132455","display_name":"Hazard","level":2,"score":0.4521487057209015},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3830867409706116},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07150387763977051},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.067639","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.067639","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.067639","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.067639","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1973663667","https://openalex.org/W1977351805","https://openalex.org/W2026944355","https://openalex.org/W2044261645","https://openalex.org/W2126052291","https://openalex.org/W2165419732","https://openalex.org/W2205158676","https://openalex.org/W2810682567","https://openalex.org/W2953282286","https://openalex.org/W2954186886","https://openalex.org/W2991604971","https://openalex.org/W3003335869","https://openalex.org/W3008924545","https://openalex.org/W3025949386","https://openalex.org/W3037891846","https://openalex.org/W3044237464","https://openalex.org/W3083117176","https://openalex.org/W3091882851","https://openalex.org/W3136311755","https://openalex.org/W3206195062","https://openalex.org/W3209169151","https://openalex.org/W4205425966","https://openalex.org/W4213052427","https://openalex.org/W4230538848","https://openalex.org/W4287218255","https://openalex.org/W4368348107","https://openalex.org/W4383219984","https://openalex.org/W4410568503"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0,171],"increasing":[1],"frequency":[2],"and":[3,43,91,110,150,157,161,185,199,240,248],"intensity":[4],"of":[5,118,144,164,188,224,232],"natural":[6,119],"disasters":[7],"necessitate":[8],"advanced":[9],"prediction":[10,194],"techniques":[11],"to":[12,67,71,87,114,131,135,213],"mitigate":[13],"potential":[14,117],"damage.":[15],"This":[16],"study":[17],"presents":[18],"a":[19,208,222],"comprehensive":[20],"multi-hazard":[21,225,238],"early":[22],"warning":[23],"framework":[24,235],"by":[25,39,196],"integrating":[26],"the":[27,49,53,85,116,136,142,154,167,176,186,189,193,215,218,230,233],"multi-source":[28,33],"data":[29,34,46],"fusion":[30],"technique.":[31],"A":[32],"extraction":[35],"method":[36],"was":[37,75,211],"introduced":[38],"extracting":[40],"pressure":[41],"level":[42],"average":[44,108,158],"precipitation":[45,159],"based":[47],"on":[48,125],"hazard":[50,106,111,155,168],"event":[51],"from":[52,129,217],"Cooperative":[54],"Open":[55],"Online":[56],"Landslide":[57],"Repository":[58],"(COOLR)":[59],"dataset":[60],"across":[61,93],"multiple":[62,94],"temporal":[63,127],"intervals":[64],"(12":[65],"h":[66,69,133],"1":[68,132],"prior":[70,134],"events).":[72],"Feature":[73],"engineering":[74],"performed":[76],"using":[77],"Choquet":[78],"fuzzy":[79,190],"integral-based":[80],"importance":[81],"scoring,":[82],"which":[83],"enables":[84],"model":[86,178],"account":[88],"for":[89,105,153,166,202,246],"interactions":[90],"uncertainty":[92],"features.":[95],"Three":[96],"individual":[97,139,205,219],"Long":[98],"Short-Term":[99],"Memory":[100],"(LSTM)":[101],"models":[102,122,140,220],"were":[103,123],"trained":[104,124],"location,":[107],"precipitation,":[109],"category":[112,169],"(i.e.,":[113],"detect":[115],"disasters).":[120],"These":[121,138,227],"varying":[126],"scales":[128],"12":[130],"event.":[137],"achieved":[141],"performance":[143],"Mean":[145],"Absolute":[146],"Error":[147],"(MAE)":[148],"2.2":[149],"3.2,":[151],"respectively,":[152,201],"location":[156],"models,":[160,184],"an":[162],"F1-score":[163],"0.825":[165],"model.":[170],"results":[172],"also":[173],"indicate":[174],"that":[175],"LSTM":[177],"outperformed":[179],"traditional":[180],"Machine":[181],"Learning":[182],"(ML)":[183],"use":[187],"integral":[191],"enhanced":[192],"capability":[195],"8.12%,":[197],"2.6%,":[198],"6.37%,":[200],"all":[203],"three":[204],"models.":[206],"Furthermore,":[207],"rule-based":[209],"algorithm":[210],"developed":[212],"synthesize":[214],"outputs":[216],"into":[221],"grid":[223],"warnings.":[226],"findings":[228],"underscore":[229],"effectiveness":[231],"proposed":[234],"in":[236],"advancing":[237],"forecasting":[239],"situational":[241],"awareness,":[242],"offering":[243],"valuable":[244],"support":[245],"timely":[247],"data-driven":[249],"emergency":[250],"response":[251],"planning.":[252]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}
