{"id":"https://openalex.org/W7125684434","doi":"https://doi.org/10.1109/exp.at2565440.2025.11348250","title":"Evaluating Machine Learning Models for Wildfire Prediction Across Different Geographic Regions","display_name":"Evaluating Machine Learning Models for Wildfire Prediction Across Different Geographic Regions","publication_year":2025,"publication_date":"2025-09-03","ids":{"openalex":"https://openalex.org/W7125684434","doi":"https://doi.org/10.1109/exp.at2565440.2025.11348250"},"language":null,"primary_location":{"id":"doi:10.1109/exp.at2565440.2025.11348250","is_oa":false,"landing_page_url":"https://doi.org/10.1109/exp.at2565440.2025.11348250","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 7th Experiment@ International Conference (exp.at'25)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123879190","display_name":"Jos\u00e9 Silva","orcid":null},"institutions":[{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Jos\u00e9 Silva","raw_affiliation_strings":["University of Coimbra, CISUC/LASI,Coimbra,Portugal"],"affiliations":[{"raw_affiliation_string":"University of Coimbra, CISUC/LASI,Coimbra,Portugal","institution_ids":["https://openalex.org/I76903346"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055392122","display_name":"Jacinto Estima","orcid":null},"institutions":[{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Jacinto Estima","raw_affiliation_strings":["University of Coimbra, CISUC/LASI,Coimbra,Portugal"],"affiliations":[{"raw_affiliation_string":"University of Coimbra, CISUC/LASI,Coimbra,Portugal","institution_ids":["https://openalex.org/I76903346"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123840153","display_name":"Alberto Cardoso","orcid":null},"institutions":[{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Alberto Cardoso","raw_affiliation_strings":["University of Coimbra, CISUC/LASI,Coimbra,Portugal"],"affiliations":[{"raw_affiliation_string":"University of Coimbra, CISUC/LASI,Coimbra,Portugal","institution_ids":["https://openalex.org/I76903346"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5123879190"],"corresponding_institution_ids":["https://openalex.org/I76903346"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68837005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"211","last_page":"216"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":0.9771999716758728,"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":0.9771999716758728,"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":0.010400000028312206,"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":0.0013000000035390258,"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/adaptability","display_name":"Adaptability","score":0.7290999889373779},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6093000173568726},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5741000175476074},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4587000012397766},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.44449999928474426},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43869999051094055},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.3928999900817871}],"concepts":[{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.7290999889373779},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7179999947547913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6546000242233276},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6093000173568726},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6040999889373779},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5741000175476074},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4587000012397766},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.44449999928474426},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43869999051094055},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.34689998626708984},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2671999931335449},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/exp.at2565440.2025.11348250","is_oa":false,"landing_page_url":"https://doi.org/10.1109/exp.at2565440.2025.11348250","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 7th Experiment@ International Conference (exp.at'25)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2993247229","https://openalex.org/W4291824800","https://openalex.org/W4312454866","https://openalex.org/W4395009837"],"related_works":[],"abstract_inverted_index":{"Wildfires":[0],"pose":[1],"significant":[2],"environmental,":[3],"social,":[4],"and":[5,53,92,103,116],"economic":[6],"challenges,":[7],"making":[8],"their":[9,100],"accurate":[10],"prediction":[11],"crucial":[12],"for":[13,62,145],"risk":[14],"management.":[15],"While":[16],"several":[17],"studies":[18],"have":[19],"explored":[20],"wildfire":[21,63,107],"forecasting":[22],"using":[23,79,140],"machine":[24,46],"learning,":[25],"most":[26],"of":[27,44,151],"them":[28],"focus":[29],"on":[30],"individual":[31],"models":[32,89,114],"without":[33],"systematically":[34],"comparing":[35],"different":[36,76],"training":[37,154],"strategies.":[38,155],"This":[39],"study":[40],"evaluates":[41],"the":[42,149],"performance":[43,73,122,144],"four":[45,80],"learning":[47,113],"models:":[48],"Random":[49],"Forest,":[50],"XGBoost,":[51],"LSTM,":[52],"BiLSTM,":[54],"to":[55,98,105,133],"determine":[56],"which":[57],"model":[58,72],"is":[59],"best":[60],"suited":[61],"pre-diction.":[64],"Unlike":[65],"previous":[66],"works,":[67],"this":[68],"research":[69],"analyzes":[70],"how":[71],"varies":[74],"across":[75,123],"geographic":[77],"regions,":[78,124],"distinct":[81],"districts":[82],"in":[83,126],"Portugal":[84],"as":[85],"test":[86],"locations.":[87],"The":[88],"are":[90,131],"trained":[91],"tested":[93],"under":[94],"multiple":[95],"dataset":[96],"configurations":[97],"assess":[99],"generalization":[101],"capability":[102],"adaptability":[104],"unseen":[106],"occurrences.":[108],"Results":[109],"indicate":[110],"that":[111,139],"deep":[112],"(LSTM":[115],"BiLSTM)":[117],"achieve":[118],"a":[119],"more":[120,152],"consistent":[121],"particularly":[125],"southern":[127],"Portugal,":[128],"where":[129],"wildfires":[130],"harder":[132],"predict.":[134],"Additionally,":[135],"results":[136],"also":[137],"show":[138],"region-specific":[141],"data":[142],"improves":[143],"certain":[146],"areas,":[147],"highlighting":[148],"importance":[150],"localized":[153]},"counts_by_year":[],"updated_date":"2026-02-23T20:09:44.859080","created_date":"2026-01-27T00:00:00"}