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

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 ri...

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Autores principales: Méndez Garabetti, Miguel, Bianchini, Germán, Caymes Scutari, Paola, Tardivo, María
Formato: Artículo acceptedVersion
Lenguaje:Inglés
Publicado: 2023
Materias:
Wildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical System
Acceso en línea:http://hdl.handle.net/20.500.12272/8007
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-8007
record_format dspace
spelling I68-R174-20.500.12272-80072023-06-08T13:27:32Z Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic Méndez Garabetti, Miguel Bianchini, Germán Caymes Scutari, Paola Tardivo, María Wildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical System 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. Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina Peer Reviewed 2023-06-08T13:27:32Z 2023-06-08T13:27:32Z 2016-03-25 info:eu-repo/semantics/article acceptedVersion Fire Safety Journal (FSJ) (Vol 82) 0379-7112 http://hdl.handle.net/20.500.12272/8007 eng PID 3939 openAccess http://creativecommons.org/publicdomain/zero/1.0/ CC0 1.0 Universal Universidad Tecnológica Nacional. Facultad Regional Mendoza Atribución pdf Fire Safety Journal (FSJ) (82)49-62 (2016)
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
topic Wildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical System
spellingShingle Wildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical System
Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
topic_facet Wildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical System
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.
format Artículo
acceptedVersion
author Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
author_facet Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, María
author_sort Méndez Garabetti, Miguel
title Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
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
publishDate 2023
url http://hdl.handle.net/20.500.12272/8007
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