ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters

Autores
Méndez Garabetti, Miguel; Bianchini, Germán; Caymes Scutari, Paola; Tardivo, María; Gil Costa, Verónica
Año de publicación
2017
Idioma
español castellano
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Abstract. Wildfires devastate thousands forests acres every year around the world. Fire behavior prediction is a useful tool to cooperate in the coordination, mitigation and management of available resources to fight against this type of contingencies. However, the prediction of this phenomenon is usually a difficult task due to the uncertainty in the prediction process. Therefore, several methods of uncertainty reduction have been developed, such as the Evolutionary Statistical System with Island Models based on Evolutionary Algorithms (ESSIM-EA). ESSIMEA focuses its operation on an Evolutionary Parallel Algorithm based on islands, in which the same configuration of evolutionary parameters is used. In this work we present an extension of the ESSIM-EA that allows each island to select an independent configuration of evolutionary parameters. The heterogeneous configuration proposed, at the island level, with the original methodology in three cases of controlled fires has been contrasted. The results show that the proposed ESSIM-EA extension allows to improve the quality of prediction and to reduce processing times.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Materia
Wildfire prediction, HPC, Uncertainty reduction, Metaheuris- tics.
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2023-06-08T16:29:07Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/8016
Acceder
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spelling ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary ParametersMéndez Garabetti, MiguelBianchini, GermánCaymes Scutari, PaolaTardivo, MaríaGil Costa, VerónicaWildfire prediction, HPC, Uncertainty reduction, Metaheuris- tics.Abstract. Wildfires devastate thousands forests acres every year around the world. Fire behavior prediction is a useful tool to cooperate in the coordination, mitigation and management of available resources to fight against this type of contingencies. However, the prediction of this phenomenon is usually a difficult task due to the uncertainty in the prediction process. Therefore, several methods of uncertainty reduction have been developed, such as the Evolutionary Statistical System with Island Models based on Evolutionary Algorithms (ESSIM-EA). ESSIMEA focuses its operation on an Evolutionary Parallel Algorithm based on islands, in which the same configuration of evolutionary parameters is used. In this work we present an extension of the ESSIM-EA that allows each island to select an independent configuration of evolutionary parameters. The heterogeneous configuration proposed, at the island level, with the original methodology in three cases of controlled fires has been contrasted. The results show that the proposed ESSIM-EA extension allows to improve the quality of prediction and to reduce processing times.Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina2023-06-08T16:29:07Z2023-06-08T16:29:07Z2017-10-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfXXIII Congreso Argentino de Ciencias de la Computaciónhttp://hdl.handle.net/20.500.12272/8016spaPID 3939info:eu-repo/semantics/openAccess2023-06-08T16:29:07Zhttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalUniversidad Tecnológica Nacional. Facultad Regional MendozaAtribuciónreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-10-01T11:58:50Zoai:ria.utn.edu.ar:20.500.12272/8016instacron: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:a2026-10-01 11:58:52.019Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters
title ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters
spellingShingle ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters
Méndez Garabetti, Miguel
Wildfire prediction, HPC, Uncertainty reduction, Metaheuris- tics.
title_short ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters
title_full ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters
title_fullStr ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters
title_full_unstemmed ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters
title_sort ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters
dc.creator.none.fl_str_mv Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
Gil Costa, Verónica
author Méndez Garabetti, Miguel
author_facet Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
Gil Costa, Verónica
author_role author
author2 Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
Gil Costa, Verónica
author2_role author
author
author
author
dc.subject.none.fl_str_mv Wildfire prediction, HPC, Uncertainty reduction, Metaheuris- tics.
topic Wildfire prediction, HPC, Uncertainty reduction, Metaheuris- tics.
dc.description.none.fl_txt_mv Abstract. Wildfires devastate thousands forests acres every year around the world. Fire behavior prediction is a useful tool to cooperate in the coordination, mitigation and management of available resources to fight against this type of contingencies. However, the prediction of this phenomenon is usually a difficult task due to the uncertainty in the prediction process. Therefore, several methods of uncertainty reduction have been developed, such as the Evolutionary Statistical System with Island Models based on Evolutionary Algorithms (ESSIM-EA). ESSIMEA focuses its operation on an Evolutionary Parallel Algorithm based on islands, in which the same configuration of evolutionary parameters is used. In this work we present an extension of the ESSIM-EA that allows each island to select an independent configuration of evolutionary parameters. The heterogeneous configuration proposed, at the island level, with the original methodology in three cases of controlled fires has been contrasted. The results show that the proposed ESSIM-EA extension allows to improve the quality of prediction and to reduce processing times.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
description Abstract. Wildfires devastate thousands forests acres every year around the world. Fire behavior prediction is a useful tool to cooperate in the coordination, mitigation and management of available resources to fight against this type of contingencies. However, the prediction of this phenomenon is usually a difficult task due to the uncertainty in the prediction process. Therefore, several methods of uncertainty reduction have been developed, such as the Evolutionary Statistical System with Island Models based on Evolutionary Algorithms (ESSIM-EA). ESSIMEA focuses its operation on an Evolutionary Parallel Algorithm based on islands, in which the same configuration of evolutionary parameters is used. In this work we present an extension of the ESSIM-EA that allows each island to select an independent configuration of evolutionary parameters. The heterogeneous configuration proposed, at the island level, with the original methodology in three cases of controlled fires has been contrasted. The results show that the proposed ESSIM-EA extension allows to improve the quality of prediction and to reduce processing times.
publishDate 2017
dc.date.none.fl_str_mv 2017-10-09
2023-06-08T16:29:07Z
2023-06-08T16:29:07Z
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 XXIII Congreso Argentino de Ciencias de la Computación
http://hdl.handle.net/20.500.12272/8016
identifier_str_mv XXIII Congreso Argentino de Ciencias de la Computación
url http://hdl.handle.net/20.500.12272/8016
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv PID 3939
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2023-06-08T16:29:07Z
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 2023-06-08T16:29:07Z
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 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
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score 13.365483

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