A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction

Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire ropagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular co...

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Autores principales: Tardivo, María, Caymes Scutari, Paola, Bianchini, Germán, Méndez Garabetti, Miguel, Cencerrado, Andrés, Cortés, Ana
Formato: Artículo acceptedVersion
Lenguaje:Inglés
Publicado: 2023
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Acceso en línea:http://hdl.handle.net/20.500.12272/7952
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id I68-R174-20.500.12272-7952
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spelling I68-R174-20.500.12272-79522023-06-06T14:33:08Z A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction Tardivo, María Caymes Scutari, Paola Bianchini, Germán Méndez Garabetti, Miguel Cencerrado, Andrés Cortés, Ana Forest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance Computing Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire ropagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) estimate precisely, and there is a high degree of uncertainty in many of them. This uncer-tainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it to predict the fire propagation. However, these parameters are usually difficult to measure or is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction. Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina 2023-06-06T14:33:08Z 2023-06-06T14:33:08Z 2017-06-12 info:eu-repo/semantics/article acceptedVersion International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland http://hdl.handle.net/20.500.12272/7952 10.1016/j.procs.2017.05.252. eng PID 3939 openAccess http://creativecommons.org/publicdomain/zero/1.0/ CC0 1.0 Universal Universidad Tecnológica Nacional. Facultad Regional Mendoza Atribución pdf Procedia Computer Science (nª 108): 2018-2027 (2017)
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
topic Forest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance Computing
spellingShingle Forest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance Computing
Tardivo, María
Caymes Scutari, Paola
Bianchini, Germán
Méndez Garabetti, Miguel
Cencerrado, Andrés
Cortés, Ana
A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
topic_facet Forest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance Computing
description Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire ropagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) estimate precisely, and there is a high degree of uncertainty in many of them. This uncer-tainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it to predict the fire propagation. However, these parameters are usually difficult to measure or is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction.
format Artículo
acceptedVersion
author Tardivo, María
Caymes Scutari, Paola
Bianchini, Germán
Méndez Garabetti, Miguel
Cencerrado, Andrés
Cortés, Ana
author_facet Tardivo, María
Caymes Scutari, Paola
Bianchini, Germán
Méndez Garabetti, Miguel
Cencerrado, Andrés
Cortés, Ana
author_sort Tardivo, María
title A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
title_short A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
title_full A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
title_fullStr A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
title_full_unstemmed A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
title_sort comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
publishDate 2023
url http://hdl.handle.net/20.500.12272/7952
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