{"id":"https://openalex.org/W4376139665","doi":"https://doi.org/10.3390/s23104628","title":"Enhancing Safety and Efficiency in Firefighting Operations via Deep Learning and Temperature Forecasting Modeling in Autonomous Unit","display_name":"Enhancing Safety and Efficiency in Firefighting Operations via Deep Learning and Temperature Forecasting Modeling in Autonomous Unit","publication_year":2023,"publication_date":"2023-05-10","ids":{"openalex":"https://openalex.org/W4376139665","doi":"https://doi.org/10.3390/s23104628","pmid":"https://pubmed.ncbi.nlm.nih.gov/37430542"},"language":"en","primary_location":{"id":"doi:10.3390/s23104628","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23104628","pdf_url":"https://www.mdpi.com/1424-8220/23/10/4628/pdf?version=1683713396","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"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":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/23/10/4628/pdf?version=1683713396","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080416804","display_name":"Adenrele A. Ishola","orcid":null},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adenrele A. Ishola","raw_affiliation_strings":["Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071972698","display_name":"Damian Valles","orcid":"https://orcid.org/0000-0002-5684-9498"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Damian Valles","raw_affiliation_strings":["Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA"],"raw_orcid":"https://orcid.org/0000-0002-5684-9498","affiliations":[{"raw_affiliation_string":"Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA","institution_ids":["https://openalex.org/I13511017"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071972698"],"corresponding_institution_ids":["https://openalex.org/I13511017"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":2.3457,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.86984059,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"23","issue":"10","first_page":"4628","last_page":"4628"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9952999949455261,"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":0.9952999949455261,"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/T11317","display_name":"Fire dynamics and safety research","score":0.972000002861023,"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/T11357","display_name":"Risk and Safety Analysis","score":0.9634000062942505,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.7636194229125977},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6225267648696899},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.542619526386261},{"id":"https://openalex.org/keywords/extrapolation","display_name":"Extrapolation","score":0.5215368270874023},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5058881640434265},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5040163397789001},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4592876136302948},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.4404316544532776},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.40119948983192444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40063393115997314},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33914458751678467},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2579424977302551},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.20244458317756653},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15117233991622925},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10543611645698547}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7636194229125977},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6225267648696899},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.542619526386261},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.5215368270874023},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5058881640434265},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5040163397789001},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4592876136302948},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4404316544532776},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.40119948983192444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40063393115997314},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33914458751678467},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2579424977302551},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.20244458317756653},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15117233991622925},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10543611645698547}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/s23104628","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23104628","pdf_url":"https://www.mdpi.com/1424-8220/23/10/4628/pdf?version=1683713396","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"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":"Sensors","raw_type":"journal-article"},{"id":"pmid:37430542","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37430542","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10224561","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10224561","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10224561/pdf/sensors-23-04628.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:80070d1d2e48412c8739b800b60a4719","is_oa":true,"landing_page_url":"https://doaj.org/article/80070d1d2e48412c8739b800b60a4719","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 23, Iss 10, p 4628 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/23/10/4628/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s23104628","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 23; Issue 10; Pages: 4628","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s23104628","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23104628","pdf_url":"https://www.mdpi.com/1424-8220/23/10/4628/pdf?version=1683713396","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"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":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7509279368","display_name":null,"funder_award_id":"4202981000","funder_id":"https://openalex.org/F4320312927","funder_display_name":"Dell EMC"}],"funders":[{"id":"https://openalex.org/F4320312927","display_name":"Dell EMC","ror":"https://ror.org/05rejmm18"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4376139665.pdf"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1533970595","https://openalex.org/W2026536250","https://openalex.org/W2047715812","https://openalex.org/W2094090240","https://openalex.org/W2117190680","https://openalex.org/W2126822753","https://openalex.org/W2322846016","https://openalex.org/W2552464054","https://openalex.org/W2905502034","https://openalex.org/W2909815557","https://openalex.org/W2995041800","https://openalex.org/W3003695670","https://openalex.org/W3037488023","https://openalex.org/W3126397679","https://openalex.org/W3127341877","https://openalex.org/W3131080578","https://openalex.org/W3160580848","https://openalex.org/W3163920046","https://openalex.org/W3209648160","https://openalex.org/W4210764571","https://openalex.org/W4282964834","https://openalex.org/W4285101200","https://openalex.org/W4293235087","https://openalex.org/W6631885290"],"related_works":["https://openalex.org/W1968270095","https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2220129715","https://openalex.org/W4296478327","https://openalex.org/W2042397106","https://openalex.org/W2669956259","https://openalex.org/W4249005693"],"abstract_inverted_index":{"Firefighters":[0],"face":[1],"numerous":[2],"challenges":[3,28],"when":[4,69],"entering":[5],"burning":[6,56,96,136,308,353],"structures":[7],"to":[8,47,73,89,107,153,243,314,348,367],"rescue":[9],"trapped":[10],"victims,":[11],"assess":[12],"the":[13,21,55,78,91,112,124,131,135,142,157,170,178,189,216,257,261,265,294,301,358],"conditions":[14],"of":[15,80,114,130,166,235,249,300,303,360],"a":[16,95,115,147,196,307,338],"residential":[17],"structure,":[18],"and":[19,36,44,52,67,75,98,156,184,269,275,321,328,342,364,371],"extinguish":[20],"fire":[22,316],"as":[23,25,177,240],"quickly":[24],"possible.":[26],"These":[27],"include":[29],"extreme":[30],"temperatures,":[31],"smoke,":[32],"toxic":[33],"gases,":[34],"explosions,":[35],"falling":[37],"objects,":[38],"which":[39],"can":[40,58,187],"hinder":[41],"their":[42,48,65],"efficiency":[43],"pose":[45],"risks":[46],"safety.":[49],"Accurate":[50],"information":[51],"data":[53,211],"about":[54,64],"site":[57,97],"help":[59],"firefighters":[60],"make":[61],"informed":[62],"decisions":[63],"duties":[66],"determine":[68],"it":[70,290],"is":[71,175,291,336],"safe":[72],"enter":[74],"evacuate,":[76],"reducing":[77],"likelihood":[79],"casualties.":[81],"This":[82,332,355],"research":[83,282,312,356],"presents":[84],"unsupervised":[85,202],"deep":[86,326,362],"learning":[87,327,363],"(DL)":[88],"classify":[90,315],"danger":[92,132],"levels":[93,133,320],"at":[94,163,172,251],"an":[99,128,164,201,232,247],"autoregressive":[100,217,343],"integrated":[101,218,344],"moving":[102,219,345],"average":[103,220,346],"(ARIMA)":[104,221],"prediction":[105,139,210],"model":[106,296],"forecast":[108,141],"temperature":[109,145,160,171,179,304,323,350],"changes":[110,158,305],"using":[111,200,215,260,325,337,361],"extrapolation":[113],"random":[116,223,266,339],"forest":[117,224,267,340],"regressor.":[118],"The":[119,138,209,227,310],"DL":[120,203],"classifier":[121],"algorithms":[122],"provide":[123],"chief":[125],"firefighter":[126,369],"with":[127,182,222,231,246],"awareness":[129],"in":[134,144,159,277,306,352],"compartment.":[137],"models":[140,272,347],"rise":[143],"from":[146,150],"height":[148],"ranging":[149],"0.6":[151],"m":[152,155],"2.6":[154,167],"over":[161],"time":[162],"altitude":[165,174],"m.":[168],"Predicting":[169],"this":[173,278,286],"critical":[176],"increases":[180],"faster":[181],"height,":[183],"elevated":[185],"temperatures":[186],"weaken":[188],"building's":[190],"structural":[191],"material.":[192],"We":[193],"also":[194],"investigated":[195],"new":[197],"classification":[198,258],"method":[199],"autoencoder":[204],"artificial":[205],"neural":[206],"network":[207],"(AE-ANN).":[208],"analytical":[212],"approach":[213],"included":[214],"regression":[225],"implementation.":[226],"proposed":[228,311],"AE-ANN":[229],"model,":[230],"accuracy":[233,248,254],"score":[234],"0.869,":[236],"did":[237],"not":[238,284],"perform":[239],"well":[241],"compared":[242],"previous":[244],"work,":[245,279],"0.989,":[250],"achieving":[252],"high":[253],"scores":[255],"for":[256],"task":[259],"same":[262],"dataset.":[263],"However,":[264,293],"regressor":[268,341],"our":[270],"ARIMA":[271,295],"are":[273],"analyzed":[274],"evaluated":[276],"while":[280],"other":[281],"has":[283],"utilized":[285],"dataset,":[287],"even":[288],"though":[289],"open-sourced.":[292],"demonstrated":[297],"remarkable":[298],"predictions":[299],"trends":[302,351],"site.":[309],"aims":[313],"sites":[317],"into":[318],"dangerous":[319],"predict":[322,349],"progression":[324],"predictive":[329,365],"modeling":[330,366],"techniques.":[331],"research's":[333],"main":[334],"contribution":[335],"sites.":[354],"demonstrates":[357],"potential":[359],"enhance":[368],"safety":[370],"decision-making":[372],"processes.":[373]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}