ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration

Forest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and precision. The ability to predict the forest fire behaviour constitutes an important to...

Descripción completa

Detalles Bibliográficos
Autores principales: Méndez Garabetti, Miguel, Bianchini, Germán, Caymes Scutari, Paola, Tardivo, M
Formato: Artículo acceptedVersion
Lenguaje:Español
Publicado: 2023
Materias:
Forest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, Computing
Acceso en línea:http://hdl.handle.net/20.500.12272/8036
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-8036
record_format dspace
spelling I68-R174-20.500.12272-80362023-06-12T14:00:25Z ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration Méndez Garabetti, Miguel Bianchini, Germán Caymes Scutari, Paola Tardivo, M Forest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, Computing Forest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and precision. The ability to predict the forest fire behaviour constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. For this reason, prediction methods should be configured to operate as efficiently as possible. In this paper, a calibration study of EvolutionaryStatistical System with Island Model’s evolutionary parameters is presented (ESS-IM). ESS-IM is a general-parallel uncertainty reduction method applied to the forest fires spread prediction. Index Terms—forest fire spread prediction, parallel evolutionary algorithms, parameters tuning, high performance computing. Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina Peer Reviewed 2023-06-12T14:00:25Z 2023-06-12T14:00:25Z 2016-10-14 info:eu-repo/semantics/article acceptedVersion http://hdl.handle.net/20.500.12272/8036 10.1109/SCCC.2016.7836007 spa PID3939 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 Forest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, Computing
spellingShingle Forest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, Computing
Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, M
ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
topic_facet Forest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, Computing
description Forest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and precision. The ability to predict the forest fire behaviour constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. For this reason, prediction methods should be configured to operate as efficiently as possible. In this paper, a calibration study of EvolutionaryStatistical System with Island Model’s evolutionary parameters is presented (ESS-IM). ESS-IM is a general-parallel uncertainty reduction method applied to the forest fires spread prediction. Index Terms—forest fire spread prediction, parallel evolutionary algorithms, parameters tuning, high performance computing.
format Artículo
acceptedVersion
author Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, M
author_facet Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, M
author_sort Méndez Garabetti, Miguel
title ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
title_short ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
title_full ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
title_fullStr ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
title_full_unstemmed ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
title_sort ess-im applied to forest fire spread prediction: parameters tuning for a heterogeneous configuration
publishDate 2023
url http://hdl.handle.net/20.500.12272/8036
work_keys_str_mv AT mendezgarabettimiguel essimappliedtoforestfirespreadpredictionparameterstuningforaheterogeneousconfiguration
AT bianchinigerman essimappliedtoforestfirespreadpredictionparameterstuningforaheterogeneousconfiguration
AT caymesscutaripaola essimappliedtoforestfirespreadpredictionparameterstuningforaheterogeneousconfiguration
AT tardivom essimappliedtoforestfirespreadpredictionparameterstuningforaheterogeneousconfiguration
_version_ 1768720914169462784

Ejemplares similares