{"id":"https://openalex.org/W4416228070","doi":"https://doi.org/10.3390/bdcc9110290","title":"Wildfire Prediction in British Columbia Using Machine Learning and Deep Learning Models: A Data-Driven Framework","display_name":"Wildfire Prediction in British Columbia Using Machine Learning and Deep Learning Models: A Data-Driven Framework","publication_year":2025,"publication_date":"2025-11-14","ids":{"openalex":"https://openalex.org/W4416228070","doi":"https://doi.org/10.3390/bdcc9110290"},"language":"en","primary_location":{"id":"doi:10.3390/bdcc9110290","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc9110290","pdf_url":null,"source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"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":"Big Data and Cognitive Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/bdcc9110290","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120401604","display_name":"Maryam Nasourinia","orcid":null},"institutions":[{"id":"https://openalex.org/I52353378","display_name":"Laurentian University","ror":"https://ror.org/03rcwtr18","country_code":"CA","type":"education","lineage":["https://openalex.org/I52353378"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Maryam Nasourinia","raw_affiliation_strings":["School of Engineering and Computer Science, Laurentian University, Sudbury, ON P3E 2C6, Canada"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Computer Science, Laurentian University, Sudbury, ON P3E 2C6, Canada","institution_ids":["https://openalex.org/I52353378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035585945","display_name":"Kalpdrum Passi","orcid":"https://orcid.org/0000-0002-7155-7901"},"institutions":[{"id":"https://openalex.org/I52353378","display_name":"Laurentian University","ror":"https://ror.org/03rcwtr18","country_code":"CA","type":"education","lineage":["https://openalex.org/I52353378"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Kalpdrum Passi","raw_affiliation_strings":["School of Engineering and Computer Science, Laurentian University, Sudbury, ON P3E 2C6, Canada"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Computer Science, Laurentian University, Sudbury, ON P3E 2C6, Canada","institution_ids":["https://openalex.org/I52353378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5035585945"],"corresponding_institution_ids":["https://openalex.org/I52353378"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33669694,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":"11","first_page":"290","last_page":"290"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":0.95660001039505,"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/T10555","display_name":"Fire effects on ecosystems","score":0.95660001039505,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.017799999564886093,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10535","display_name":"Landslides and related hazards","score":0.005900000222027302,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/deep-learning","display_name":"Deep learning","score":0.6524999737739563},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6342999935150146},{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.5927000045776367},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.5892999768257141},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5077000260353088},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.44200000166893005},{"id":"https://openalex.org/keywords/climate-change","display_name":"Climate change","score":0.42410001158714294}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7060999870300293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6976000070571899},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6524999737739563},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6342999935150146},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.5927000045776367},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.5892999768257141},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5077000260353088},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4401000142097473},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.42410001158714294},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C107826830","wikidata":"https://www.wikidata.org/wiki/Q929380","display_name":"Environmental resource management","level":1,"score":0.3546000123023987},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C2779296788","wikidata":"https://www.wikidata.org/wiki/Q5326904","display_name":"Early warning system","level":3,"score":0.34040001034736633},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2962000072002411},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.25690001249313354}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/bdcc9110290","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc9110290","pdf_url":null,"source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"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":"Big Data and Cognitive Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:316d1a60de874466aaf799db4ed56bfc","is_oa":true,"landing_page_url":"https://doaj.org/article/316d1a60de874466aaf799db4ed56bfc","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":"Big Data and Cognitive Computing, Vol 9, Iss 11, p 290 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/bdcc9110290","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc9110290","pdf_url":null,"source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"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":"Big Data and Cognitive Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1989364707","https://openalex.org/W2026415375","https://openalex.org/W2115506886","https://openalex.org/W2132424470","https://openalex.org/W2135072911","https://openalex.org/W2154710695","https://openalex.org/W2789751949","https://openalex.org/W2900842202","https://openalex.org/W2986604116","https://openalex.org/W3007497458","https://openalex.org/W3008626511","https://openalex.org/W4205341965","https://openalex.org/W4294189281","https://openalex.org/W4386703530","https://openalex.org/W4392305441","https://openalex.org/W4412526453","https://openalex.org/W4413297572"],"related_works":[],"abstract_inverted_index":{"Wildfires":[0],"pose":[1],"a":[2,50],"growing":[3],"threat":[4],"to":[5,37,54,192],"ecosystems,":[6],"infrastructure,":[7],"and":[8,26,42,62,103,107,116,124,130,148,172,197],"public":[9],"safety,":[10],"particularly":[11],"in":[12,30,202],"the":[13,23,81,136,176,186],"province":[14],"of":[15,28,76,143,146,150,158,180,188],"British":[16,203],"Columbia":[17],"(BC),":[18],"Canada.":[19],"In":[20],"recent":[21],"years,":[22],"frequency,":[24],"severity,":[25],"scale":[27],"wildfires":[29],"BC":[31],"have":[32],"increased":[33],"significantly,":[34],"largely":[35],"due":[36],"climate":[38,91],"change,":[39],"human":[40],"activity,":[41],"changing":[43],"land":[44],"use":[45],"patterns.":[46],"This":[47],"study":[48,161],"presents":[49],"comprehensive,":[51],"data-driven":[52,189],"approach":[53],"wildfire":[55,77,181,200],"prediction,":[56],"leveraging":[57],"advanced":[58],"machine":[59],"learning":[60,64],"(ML)":[61],"deep":[63],"(DL)":[65],"techniques.":[66],"A":[67],"high-resolution":[68],"dataset":[69,95],"was":[70],"constructed":[71],"by":[72],"integrating":[73],"five":[74,117],"years":[75],"incident":[78],"records":[79,102],"from":[80],"Canadian":[82],"Wildland":[83],"Fire":[84],"Information":[85],"System":[86],"(CWFIS)":[87],"with":[88,140],"ERA5":[89],"reanalysis":[90],"data.":[92],"The":[93,132,160],"final":[94],"comprises":[96],"more":[97],"than":[98],"3.6":[99],"million":[100],"spatiotemporal":[101],"148":[104],"environmental,":[105],"meteorological,":[106],"geospatial":[108],"features.":[109],"Six":[110],"feature":[111],"selection":[112],"techniques":[113],"were":[114],"evaluated,":[115],"predictive":[118,138],"models\u2014Random":[119],"Forest,":[120],"XGBoost,":[121],"LightGBM,":[122],"CatBoost,":[123],"an":[125,141,156],"RNN":[126],"+":[127],"LSTM\u2014were":[128],"trained":[129],"compared.":[131],"CatBoost":[133],"model":[134],"achieved":[135,155],"highest":[137],"performance":[139],"accuracy":[142,157],"93.4%,":[144],"F1-score":[145],"92.1%,":[147],"ROC-AUC":[149],"0.94,":[151],"while":[152],"Random":[153],"Forest":[154],"92.6%.":[159],"identifies":[162],"key":[163],"environmental":[164],"variables,":[165],"including":[166],"surface":[167],"temperature,":[168],"humidity,":[169],"wind":[170],"speed,":[171],"soil":[173],"moisture,":[174],"as":[175],"most":[177],"influential":[178],"predictors":[179],"occurrence.":[182],"These":[183],"findings":[184],"highlight":[185],"potential":[187],"AI":[190],"frameworks":[191],"support":[193],"early":[194],"warning":[195],"systems":[196],"enhance":[198],"operational":[199],"management":[201],"Columbia.":[204]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
