Predicting burned areas of forest fires

Detalhes bibliográficos
Autor(a) principal: Castelli, Mauro
Data de Publicação: 2015
Outros Autores: Vanneschi, Leonardo, Popovič, Aleš
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.4996/fireecology.1101106
Resumo: Castelli, M., Vanneschi, L., & Popovič, A. (2015). Predicting burned areas of forest fires: An artificial intelligence approach. Fire Ecology, 11(1), 106-118. https://doi.org/10.4996/fireecology.1101106
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spelling Predicting burned areas of forest firesAn artificial intelligence approachClimatic dataForest firesGenetic programmingPortugalSemanticsForestryEcology, Evolution, Behavior and SystematicsEnvironmental Science (miscellaneous)Castelli, M., Vanneschi, L., & Popovič, A. (2015). Predicting burned areas of forest fires: An artificial intelligence approach. Fire Ecology, 11(1), 106-118. https://doi.org/10.4996/fireecology.1101106Forest fires importantly influence our environment and lives. The ability of accurately predicting the area that may be involved in a forest fire event may help in optimizing fire management efforts. Given the complexity of the task, powerful computational tools are needed for predicting the amount of area that will be burned during a forest fire. The purpose of this study was to develop an intelligent system based on genetic programming for the prediction of burned areas, using only data related to the forest under analysis and meteorological data. We used geometric semantic genetic programming based on recently defined geometric semantic genetic operators for genetic programming. Experimental results, achieved using a database of 517 forest fire events between 2000 and 2003, showed the appropriateness of the proposed system for the prediction of the burned areas. In particular, results obtained with geometric semantic genetic programming were significantly better than those produced by standard genetic programming and other state of the art machine learning methods on both training and out-of-sample data. This study suggests that deeper investigation of genetic programming in the field of forest fires prediction may be productive.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNCastelli, MauroVanneschi, LeonardoPopovič, Aleš2019-05-29T22:08:16Z2015-04-012015-04-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttps://doi.org/10.4996/fireecology.1101106eng1933-9747PURE: 13515305http://www.scopus.com/inward/record.url?scp=84926339792&partnerID=8YFLogxKhttps://doi.org/10.4996/fireecology.1101106info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:33:36Zoai:run.unl.pt:10362/71194Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:35:11.524607Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predicting burned areas of forest fires
An artificial intelligence approach
title Predicting burned areas of forest fires
spellingShingle Predicting burned areas of forest fires
Castelli, Mauro
Climatic data
Forest fires
Genetic programming
Portugal
Semantics
Forestry
Ecology, Evolution, Behavior and Systematics
Environmental Science (miscellaneous)
title_short Predicting burned areas of forest fires
title_full Predicting burned areas of forest fires
title_fullStr Predicting burned areas of forest fires
title_full_unstemmed Predicting burned areas of forest fires
title_sort Predicting burned areas of forest fires
author Castelli, Mauro
author_facet Castelli, Mauro
Vanneschi, Leonardo
Popovič, Aleš
author_role author
author2 Vanneschi, Leonardo
Popovič, Aleš
author2_role author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Castelli, Mauro
Vanneschi, Leonardo
Popovič, Aleš
dc.subject.por.fl_str_mv Climatic data
Forest fires
Genetic programming
Portugal
Semantics
Forestry
Ecology, Evolution, Behavior and Systematics
Environmental Science (miscellaneous)
topic Climatic data
Forest fires
Genetic programming
Portugal
Semantics
Forestry
Ecology, Evolution, Behavior and Systematics
Environmental Science (miscellaneous)
description Castelli, M., Vanneschi, L., & Popovič, A. (2015). Predicting burned areas of forest fires: An artificial intelligence approach. Fire Ecology, 11(1), 106-118. https://doi.org/10.4996/fireecology.1101106
publishDate 2015
dc.date.none.fl_str_mv 2015-04-01
2015-04-01T00:00:00Z
2019-05-29T22:08:16Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.4996/fireecology.1101106
url https://doi.org/10.4996/fireecology.1101106
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1933-9747
PURE: 13515305
http://www.scopus.com/inward/record.url?scp=84926339792&partnerID=8YFLogxK
https://doi.org/10.4996/fireecology.1101106
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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