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

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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 usu...

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Autores principales: Méndez Garabetti, Miguel, Bianchini, Germán, Caymes Scutari, Paola, Tardivo, María, Gil Costa, Verónica
Formato: Artículo acceptedVersion
Lenguaje:Español
Publicado: 2023
Materias:
Wildfire prediction, HPC, Uncertainty reduction, Metaheuris- tics.
Acceso en línea:http://hdl.handle.net/20.500.12272/8016
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-8016
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spelling I68-R174-20.500.12272-80162023-06-08T16:29:07Z ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters Méndez Garabetti, Miguel Bianchini, Germán Caymes Scutari, Paola Tardivo, María Gil Costa, Verónica Wildfire 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. Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina 2023-06-08T16:29:07Z 2023-06-08T16:29:07Z 2017-10-09 info:eu-repo/semantics/article acceptedVersion XXIII Congreso Argentino de Ciencias de la Computación http://hdl.handle.net/20.500.12272/8016 spa PID 3939 openAccess http://creativecommons.org/publicdomain/zero/1.0/ CC0 1.0 Universal Universidad Tecnológica Nacional. Facultad Regional Mendoza Atribución pdf
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Español
topic Wildfire prediction, HPC, Uncertainty reduction, Metaheuris- tics.
spellingShingle Wildfire prediction, HPC, Uncertainty reduction, Metaheuris- tics.
Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
Gil Costa, Verónica
ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters
topic_facet Wildfire prediction, HPC, Uncertainty reduction, Metaheuris- tics.
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.
format Artículo
acceptedVersion
author Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
Gil Costa, Verónica
author_facet Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
Gil Costa, Verónica
author_sort Méndez Garabetti, Miguel
title ESSIM-EA applied to Wildfire Prediction using Heterogeneous Configuration for Evolutionary Parameters
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
publishDate 2023
url http://hdl.handle.net/20.500.12272/8016
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