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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Acceso en línea: | http://hdl.handle.net/20.500.12272/7952 |
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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) |
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Universidad Tecnológica Nacional |
institution_str |
I-68 |
repository_str |
R-174 |
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RIA - Repositorio Institucional Abierto (UTN) |
language |
Inglés |
topic |
Forest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance Computing |
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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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