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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/63485
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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 |
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conferenceObject |
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publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/63485 |
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http://sedici.unlp.edu.ar/handle/10915/63485 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/altIdentifier/isbn/978-950-34-1539-9 |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf 53-62 |
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