A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/100484 https://doi.org/10.3390/math10030300 |
Resumo: | Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions. |
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A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibrationwildfirewildfire spread predictioncalibrationgenetic algorithmevolutionary algorithmsWildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions.2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/100484http://hdl.handle.net/10316/100484https://doi.org/10.3390/math10030300eng2227-7390Pereira, JorgeMendes, Jérôme Amaro PiresJúnior, Jorge S. S.Viegas, CarlosPaulo, João Ruivoinfo: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:RCAAP2022-06-23T20:31:36Zoai:estudogeral.uc.pt:10316/100484Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:17:51.791Repositório 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 |
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration |
title |
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration |
spellingShingle |
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration Pereira, Jorge wildfire wildfire spread prediction calibration genetic algorithm evolutionary algorithms |
title_short |
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration |
title_full |
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration |
title_fullStr |
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration |
title_full_unstemmed |
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration |
title_sort |
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration |
author |
Pereira, Jorge |
author_facet |
Pereira, Jorge Mendes, Jérôme Amaro Pires Júnior, Jorge S. S. Viegas, Carlos Paulo, João Ruivo |
author_role |
author |
author2 |
Mendes, Jérôme Amaro Pires Júnior, Jorge S. S. Viegas, Carlos Paulo, João Ruivo |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Pereira, Jorge Mendes, Jérôme Amaro Pires Júnior, Jorge S. S. Viegas, Carlos Paulo, João Ruivo |
dc.subject.por.fl_str_mv |
wildfire wildfire spread prediction calibration genetic algorithm evolutionary algorithms |
topic |
wildfire wildfire spread prediction calibration genetic algorithm evolutionary algorithms |
description |
Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 |
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 |
http://hdl.handle.net/10316/100484 http://hdl.handle.net/10316/100484 https://doi.org/10.3390/math10030300 |
url |
http://hdl.handle.net/10316/100484 https://doi.org/10.3390/math10030300 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2227-7390 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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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1799134074269335552 |