{"id":"https://openalex.org/W1490324539","doi":"https://doi.org/10.1007/978-3-642-55032-4_31","title":"Predicting Size of Forest Fire Using Hybrid Model","display_name":"Predicting Size of Forest Fire Using Hybrid Model","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W1490324539","doi":"https://doi.org/10.1007/978-3-642-55032-4_31","mag":"1490324539"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-642-55032-4_31","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-642-55032-4_31","pdf_url":"https://link.springer.com/content/pdf/10.1007%2F978-3-642-55032-4_31.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://link.springer.com/content/pdf/10.1007%2F978-3-642-55032-4_31.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003240225","display_name":"Guruh Fajar Shidik","orcid":"https://orcid.org/0000-0001-5314-6829"},"institutions":[{"id":"https://openalex.org/I165230279","display_name":"Universitas Gadjah Mada","ror":"https://ror.org/03ke6d638","country_code":"ID","type":"education","lineage":["https://openalex.org/I165230279"]},{"id":"https://openalex.org/I4210127958","display_name":"Universitas Dian Nuswantoro","ror":"https://ror.org/02csxcg02","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210127958"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Guruh Fajar Shidik","raw_affiliation_strings":["Universitas Dian Nuswantoro Indonesia, Universitas Gadjah Mada, Indonesia"],"affiliations":[{"raw_affiliation_string":"Universitas Dian Nuswantoro Indonesia, Universitas Gadjah Mada, Indonesia","institution_ids":["https://openalex.org/I4210127958","https://openalex.org/I165230279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072284220","display_name":"Khabib Mustofa","orcid":"https://orcid.org/0000-0002-8659-8677"},"institutions":[{"id":"https://openalex.org/I4210127958","display_name":"Universitas Dian Nuswantoro","ror":"https://ror.org/02csxcg02","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210127958"]},{"id":"https://openalex.org/I165230279","display_name":"Universitas Gadjah Mada","ror":"https://ror.org/03ke6d638","country_code":"ID","type":"education","lineage":["https://openalex.org/I165230279"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Khabib Mustofa","raw_affiliation_strings":["Universitas Dian Nuswantoro Indonesia, Universitas Gadjah Mada, Indonesia"],"affiliations":[{"raw_affiliation_string":"Universitas Dian Nuswantoro Indonesia, Universitas Gadjah Mada, Indonesia","institution_ids":["https://openalex.org/I4210127958","https://openalex.org/I165230279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5003240225"],"corresponding_institution_ids":["https://openalex.org/I165230279","https://openalex.org/I4210127958"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":null,"fwci":0.8246,"has_fulltext":true,"cited_by_count":24,"citation_normalized_percentile":{"value":0.86805953,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"316","last_page":"327"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":0.9934999942779541,"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":0.9934999942779541,"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":0.9646999835968018,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9585000276565552,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/artificial-neural-network","display_name":"Artificial neural network","score":0.6320779323577881},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6009926795959473},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.5736438632011414},{"id":"https://openalex.org/keywords/confusion-matrix","display_name":"Confusion matrix","score":0.5228943228721619},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.5073575377464294},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.4681420922279358},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.41648024320602417},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.41552430391311646},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3818042278289795},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3677191734313965},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35036084055900574},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2944489121437073},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.281504362821579},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1270819902420044}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6320779323577881},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6009926795959473},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.5736438632011414},{"id":"https://openalex.org/C138602881","wikidata":"https://www.wikidata.org/wiki/Q2709591","display_name":"Confusion matrix","level":2,"score":0.5228943228721619},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.5073575377464294},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.4681420922279358},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.41648024320602417},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.41552430391311646},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3818042278289795},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3677191734313965},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35036084055900574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2944489121437073},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.281504362821579},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1270819902420044}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/978-3-642-55032-4_31","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-642-55032-4_31","pdf_url":"https://link.springer.com/content/pdf/10.1007%2F978-3-642-55032-4_31.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},{"id":"pmh:oai:HAL:hal-01397228v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-01397228","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia), Apr 2014, Bali, Indonesia. pp.316-327, &#x27E8;10.1007/978-3-642-55032-4_31&#x27E9;","raw_type":"Conference papers"}],"best_oa_location":{"id":"doi:10.1007/978-3-642-55032-4_31","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-642-55032-4_31","pdf_url":"https://link.springer.com/content/pdf/10.1007%2F978-3-642-55032-4_31.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1490324539.pdf","grobid_xml":"https://content.openalex.org/works/W1490324539.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W259338706","https://openalex.org/W1550657683","https://openalex.org/W1570448133","https://openalex.org/W1974807642","https://openalex.org/W1977004800","https://openalex.org/W1987555141","https://openalex.org/W1990357437","https://openalex.org/W1990368529","https://openalex.org/W2003326425","https://openalex.org/W2070151867","https://openalex.org/W2070285512","https://openalex.org/W2096589080","https://openalex.org/W2113076747","https://openalex.org/W2116631292","https://openalex.org/W2123504579","https://openalex.org/W2140190241","https://openalex.org/W2154219014","https://openalex.org/W2166911901","https://openalex.org/W2198804053","https://openalex.org/W2466512847","https://openalex.org/W2543227788","https://openalex.org/W4246540242","https://openalex.org/W4252587636","https://openalex.org/W4380488575"],"related_works":["https://openalex.org/W4386799044","https://openalex.org/W2773208253","https://openalex.org/W2560646951","https://openalex.org/W4401343862","https://openalex.org/W2747837925","https://openalex.org/W3014272627","https://openalex.org/W2551890981","https://openalex.org/W3190721986","https://openalex.org/W3203638897","https://openalex.org/W4321635934"],"abstract_inverted_index":{"This":[0,180],"paper":[1],"outlines":[2],"a":[3],"hybrid":[4,50],"approach":[5],"in":[6,90,144],"data":[7,71,137],"mining":[8],"to":[9,64,77,98,138],"predict":[10],"the":[11,66,112,140,167,184],"size":[12,93,162],"of":[13,163,169,186],"forest":[14,19,164],"fire":[15,91,115,165],"using":[16,96],"meteorological":[17],"and":[18,47,56,105,152,176,200],"weather":[20],"index":[21],"(FWI)":[22],"variables":[23],"such":[24,193],"as":[25,75,123,194],"Fine":[26],"Fuel":[27],"Moisture":[28,32],"Code":[29,33,36],"(FFMC),":[30],"Duff":[31],"(DMC),":[34],"Drought":[35],"(DC),":[37],"Initial":[38],"Spread":[39],"Index":[40],"(ISI),":[41],"temperature,":[42],"Relative":[43],"Humidity":[44],"(RH),":[45],"wind":[46],"rain.":[48],"The":[49,69,82,155],"model":[51,188],"is":[52,62,121,132],"developed":[53],"with":[54,118,166,189],"clustering":[55],"classification":[57,161,191],"approaches.":[58],"Fuzzy":[59],"C-Means":[60],"(FCM)":[61],"used":[63,74],"cluster":[65],"historical":[67],"variables.":[68],"clustered":[70,95,122],"are":[72,94],"then":[73],"inputs":[76],"Back-Propagation":[78,128],"Neural":[79,129],"Network":[80,130],"classification.":[81],"label":[83,117],"dataset":[84],"having":[85],"value":[86,119],"greater":[87],"than":[88],"zero":[89,120],"area":[92,116],"FCM":[97],"produce":[99],"two":[100],"categorical":[101],"clusters,i.e.:":[102],"Light":[103,150],"Burn,":[104],"Heavy":[106,153],"Burn":[107,125,148,151],"for":[108],"its":[109],"label.":[110],"On":[111],"other":[113,190],"hand,":[114],"No":[124,147],"Area.":[126],"A":[127],"(BPNN)":[131],"trained":[133],"based":[134],"on":[135],"these":[136],"classify":[139],"output":[141],"(burn":[142],"area)":[143],"three":[145],"categories,":[146],"Area,":[149],"Burn.":[154],"experiment":[156],"shows":[157],"promising":[158],"results":[159],"depicting":[160],"accuracy":[168],"confusion":[170],"matrix":[171],"around":[172],"97,":[173],"50":[174],"%":[175],"Cohens":[177],"Kappa":[178],"0.954.":[179],"research":[181],"also":[182],"compares":[183],"performance":[185],"proposed":[187],"method":[192],"SVM,":[195],"Naive":[196],"Bayes,":[197],"DCT":[198],"Tree,":[199],"K-NN":[201],"that":[202],"showed":[203],"BPNN":[204],"have":[205],"best":[206],"performance.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
