{"id":"https://openalex.org/W7091436435","doi":"https://doi.org/10.32604/cmc.2025.069377","title":"An Improved Forest Fire Detection Model Using Audio Classification and Machine Learning","display_name":"An Improved Forest Fire Detection Model Using Audio Classification and Machine Learning","publication_year":2025,"publication_date":"2025-10-16","ids":{"openalex":"https://openalex.org/W7091436435","doi":"https://doi.org/10.32604/cmc.2025.069377"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.069377","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.069377","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.2025.069377","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Kemahyanto Exaudi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kemahyanto Exaudi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Deris Stiawan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deris Stiawan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Bhakti Yudho Suprapto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhakti Yudho Suprapto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Hanif Fakhrurroja","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hanif Fakhrurroja","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Mohd. Yazid Idris","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohd. Yazid Idris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Tami A. Alghamdi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tami A. Alghamdi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Rahmat Budiarto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahmat Budiarto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3152,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.81619018,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"86","issue":"1","first_page":"1","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.8162000179290771,"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"}},"topics":[{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.8162000179290771,"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/T10555","display_name":"Fire effects on ecosystems","score":0.11410000175237656,"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/T11309","display_name":"Music and Audio Processing","score":0.0044999998062849045,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6510999798774719},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6344000101089478},{"id":"https://openalex.org/keywords/fire-detection","display_name":"Fire detection","score":0.527899980545044},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5044999718666077},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4790000021457672},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.46459999680519104},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4564000070095062},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4198000133037567},{"id":"https://openalex.org/keywords/microphone","display_name":"Microphone","score":0.41679999232292175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6974999904632568},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6510999798774719},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6344000101089478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6129000186920166},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5403000116348267},{"id":"https://openalex.org/C2780836893","wikidata":"https://www.wikidata.org/wiki/Q19922674","display_name":"Fire detection","level":2,"score":0.527899980545044},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5044999718666077},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4790000021457672},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4564000070095062},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4198000133037567},{"id":"https://openalex.org/C2778263558","wikidata":"https://www.wikidata.org/wiki/Q46384","display_name":"Microphone","level":3,"score":0.41679999232292175},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39750000834465027},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.37400001287460327},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C2779119184","wikidata":"https://www.wikidata.org/wiki/Q294350","display_name":"ALARM","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2964000105857849},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2883000075817108},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C64922751","wikidata":"https://www.wikidata.org/wiki/Q4650799","display_name":"Audio signal","level":3,"score":0.27559998631477356},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.26930001378059387},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.25519999861717224},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.069377","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.069377","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.2025.069377","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.069377","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":[{"display_name":"Life in Land","score":0.634911298751831,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sudden":[0],"wildfires":[1],"cause":[2],"significant":[3],"global":[4],"ecological":[5],"damage.":[6],"While":[7],"satellite":[8],"imagery":[9],"has":[10],"advanced":[11],"early":[12],"fire":[13,47,84,196],"detection":[14,48,163],"and":[15,28,53,71,134,145,190],"mitigation,":[16],"image-based":[17,174],"systems":[18],"face":[19],"limitations":[20],"including":[21],"high":[22,120],"false":[23],"alarm":[24],"rates,":[25],"visual":[26,95],"obstructions,":[27],"substantial":[29],"computational":[30,169],"demands,":[31],"especially":[32],"in":[33,122,198],"complex":[34,109],"forest":[35,46,195],"terrains.":[36],"To":[37],"address":[38],"these":[39],"challenges,":[40],"this":[41],"study":[42],"proposes":[43],"a":[44,74,187],"novel":[45],"model":[49,118],"utilizing":[50],"audio":[51],"classification":[52],"machine":[54,184],"learning.":[55],"We":[56],"developed":[57],"an":[58],"audio-based":[59,158],"pipeline":[60],"using":[61],"real-world":[62],"environmental":[63,110,151],"sound":[64,103],"recordings.":[65],"Sounds":[66],"were":[67,105],"converted":[68],"into":[69],"Mel-spectrograms":[70],"classified":[72],"via":[73],"Convolutional":[75],"Neural":[76],"Network":[77],"(CNN),":[78],"enabling":[79],"the":[80,157],"capture":[81],"of":[82,100],"distinctive":[83],"acoustic":[85,180],"signatures":[86],"(e.g.,":[87],"crackling,":[88],"roaring)":[89],"that":[90,156,179],"are":[91],"minimally":[92],"impacted":[93],"by":[94],"or":[96],"weather":[97],"conditions.":[98,152],"Internet":[99],"Things":[101],"(IoT)":[102],"sensors":[104],"crucial":[106],"for":[107,193],"generating":[108],"parameters":[111],"to":[112,172],"optimize":[113],"feature":[114],"extraction.":[115],"The":[116,176],"CNN":[117],"achieved":[119],"performance":[121],"stratified":[123],"5-fold":[124],"cross-validation":[125],"(92.4%":[126],"\u00b1":[127,131],"1.6":[128],"accuracy,":[129,139],"91.2%":[130],"1.8":[132],"F1-score)":[133],"on":[135],"test":[136],"data":[137],"(94.93%":[138],"93.04%":[140],"F1-score),":[141],"with":[142,183],"98.44%":[143],"precision":[144],"88.32%":[146],"recall,":[147],"demonstrating":[148],"reliability":[149,164],"across":[150],"These":[153],"results":[154],"indicate":[155],"approach":[159],"not":[160],"only":[161],"improves":[162],"but":[165],"also":[166],"markedly":[167],"reduces":[168],"overhead":[170],"compared":[171],"traditional":[173],"methods.":[175],"findings":[177],"suggest":[178],"sensing":[181],"integrated":[182],"learning":[185],"offers":[186],"powerful,":[188],"low-cost,":[189],"efficient":[191],"solution":[192],"real-time":[194],"monitoring":[197],"complex,":[199],"dynamic":[200],"environments.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-16T00:00:00"}
