Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic

Autores
Méndez Garabetti, Miguel; Bianchini, Germán; Caymes Scutari, Paola; Tardivo, María
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Wildfires cause great losses and harms every year, some of which are often irreparable. Among the different strategies and technologies available to mitigate the effects of fire, wildfire behavior prediction may be a promising strategy. This approach allows for the identification of areas at greatest risk of being burned, thereby permitting to make decisions which in turn will help to reduce losses and damages. In this work we present an Evolutionary-Statistical System with Island Model, a new approach of the uncertainty reduction method Evolutionary-Statistical System. The operation of ESS is based on statistical analysis, parallel computing and Parallel Evolutionary Algorithms (PEA). ESS-IM empowers and broadens the search process and space by incorporating the Island Model in the metaheuristic stage (PEA), which increases the level of parallelism and, in fact, it permits to improve the quality of predictions.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Peer Reviewed
Fuente
Fire Safety Journal (FSJ) (82)49-62 (2016)
Materia
Wildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical System
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2023-06-08T13:27:32Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/8007
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristicMéndez Garabetti, MiguelBianchini, GermánCaymes Scutari, PaolaTardivo, MaríaWildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical SystemWildfires cause great losses and harms every year, some of which are often irreparable. Among the different strategies and technologies available to mitigate the effects of fire, wildfire behavior prediction may be a promising strategy. This approach allows for the identification of areas at greatest risk of being burned, thereby permitting to make decisions which in turn will help to reduce losses and damages. In this work we present an Evolutionary-Statistical System with Island Model, a new approach of the uncertainty reduction method Evolutionary-Statistical System. The operation of ESS is based on statistical analysis, parallel computing and Parallel Evolutionary Algorithms (PEA). ESS-IM empowers and broadens the search process and space by incorporating the Island Model in the metaheuristic stage (PEA), which increases the level of parallelism and, in fact, it permits to improve the quality of predictions.Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; ArgentinaPeer Reviewed2023-06-08T13:27:32Z2023-06-08T13:27:32Z2016-03-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfFire Safety Journal (FSJ) (Vol 82)0379-7112http://hdl.handle.net/20.500.12272/8007Fire Safety Journal (FSJ) (82)49-62 (2016)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica NacionalengPID 3939info:eu-repo/semantics/openAccess2023-06-08T13:27:32Zhttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalUniversidad Tecnológica Nacional. Facultad Regional MendozaAtribución2026-09-24T12:44:49Zoai:ria.utn.edu.ar:20.500.12272/8007instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:44:50.368Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
title Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
spellingShingle Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
Méndez Garabetti, Miguel
Wildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical System
title_short Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
title_full Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
title_fullStr Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
title_full_unstemmed Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
title_sort Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
dc.creator.none.fl_str_mv Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
author Méndez Garabetti, Miguel
author_facet Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
author_role author
author2 Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
author2_role author
author
author
dc.subject.none.fl_str_mv Wildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical System
topic Wildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical System
dc.description.none.fl_txt_mv Wildfires cause great losses and harms every year, some of which are often irreparable. Among the different strategies and technologies available to mitigate the effects of fire, wildfire behavior prediction may be a promising strategy. This approach allows for the identification of areas at greatest risk of being burned, thereby permitting to make decisions which in turn will help to reduce losses and damages. In this work we present an Evolutionary-Statistical System with Island Model, a new approach of the uncertainty reduction method Evolutionary-Statistical System. The operation of ESS is based on statistical analysis, parallel computing and Parallel Evolutionary Algorithms (PEA). ESS-IM empowers and broadens the search process and space by incorporating the Island Model in the metaheuristic stage (PEA), which increases the level of parallelism and, in fact, it permits to improve the quality of predictions.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Peer Reviewed
description Wildfires cause great losses and harms every year, some of which are often irreparable. Among the different strategies and technologies available to mitigate the effects of fire, wildfire behavior prediction may be a promising strategy. This approach allows for the identification of areas at greatest risk of being burned, thereby permitting to make decisions which in turn will help to reduce losses and damages. In this work we present an Evolutionary-Statistical System with Island Model, a new approach of the uncertainty reduction method Evolutionary-Statistical System. The operation of ESS is based on statistical analysis, parallel computing and Parallel Evolutionary Algorithms (PEA). ESS-IM empowers and broadens the search process and space by incorporating the Island Model in the metaheuristic stage (PEA), which increases the level of parallelism and, in fact, it permits to improve the quality of predictions.
publishDate 2016
dc.date.none.fl_str_mv 2016-03-25
2023-06-08T13:27:32Z
2023-06-08T13:27:32Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv Fire Safety Journal (FSJ) (Vol 82)
0379-7112
http://hdl.handle.net/20.500.12272/8007
identifier_str_mv Fire Safety Journal (FSJ) (Vol 82)
0379-7112
url http://hdl.handle.net/20.500.12272/8007
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PID 3939
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2023-06-08T13:27:32Z
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
Universidad Tecnológica Nacional. Facultad Regional Mendoza
Atribución
eu_rights_str_mv openAccess
rights_invalid_str_mv 2023-06-08T13:27:32Z
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
Universidad Tecnológica Nacional. Facultad Regional Mendoza
Atribución
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv Fire Safety Journal (FSJ) (82)49-62 (2016)
reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
_version_ 1877230888240021504
score 13.24418

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