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

Autores
Méndez Garabetti, Miguel; BIanchini, Germán; Caymes Scutari, Paola; Tardivo, María Laura; Gil Costa, Graciela Verónica
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
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.
Eje: XVIII Workshop de Agentes y Sistemas Inteligentes (WASI).
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Heuristic methods
wildfire prediction
HPC
uncertainty reduction
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/63485

id SEDICI_da19ad2a8b8f7c8c9a283d988c914f7e
oai_identifier_str oai:sedici.unlp.edu.ar:10915/63485
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary ParametersMéndez Garabetti, MiguelBIanchini, GermánCaymes Scutari, PaolaTardivo, María LauraGil Costa, Graciela VerónicaCiencias InformáticasHeuristic methodswildfire predictionHPCuncertainty reductionWildfires 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.Eje: XVIII Workshop de Agentes y Sistemas Inteligentes (WASI).Red de Universidades con Carreras en Informática (RedUNCI)2017-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf53-62http://sedici.unlp.edu.ar/handle/10915/63485enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-1539-9info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:08:24Zoai:sedici.unlp.edu.ar:10915/63485Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:08:25.008SEDICI (UNLP) - Universidad Nacional de La Platafalse
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
Ciencias Informáticas
Heuristic methods
wildfire prediction
HPC
uncertainty reduction
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 Laura
Gil Costa, Graciela Verónica
author Méndez Garabetti, Miguel
author_facet Méndez Garabetti, Miguel
BIanchini, Germán
Caymes Scutari, Paola
Tardivo, María Laura
Gil Costa, Graciela Verónica
author_role author
author2 BIanchini, Germán
Caymes Scutari, Paola
Tardivo, María Laura
Gil Costa, Graciela Verónica
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Heuristic methods
wildfire prediction
HPC
uncertainty reduction
topic Ciencias Informáticas
Heuristic methods
wildfire prediction
HPC
uncertainty reduction
dc.description.none.fl_txt_mv 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.
Eje: XVIII Workshop de Agentes y Sistemas Inteligentes (WASI).
Red de Universidades con Carreras en Informática (RedUNCI)
description 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
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/63485
url http://sedici.unlp.edu.ar/handle/10915/63485
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-34-1539-9
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
53-62
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844615956608843776
score 13.070432