Evolutionary-statistical system for uncertainty reduction problems in wildfires

Fire modelling is used by engineers and scientists to understand and to predict possible fire behaviour. Empirical, semi-empirical, and physical models have been developed to predict wildfire behaviour. Any of these can be used to develop simulators and tools for preventing and fighting wildfires. H...

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Autores principales: BIanchini, Germán, Méndez Garabetti, Miguel, Caymes Scutari, Paola
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2012
Materias:
Ciencias Informáticas
evolutionary-statistical system
uncertainty reduction problems
wildfires
Distributed
Parallel
Algorithms
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23618
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23618
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
evolutionary-statistical system
uncertainty reduction problems
wildfires
Distributed
Parallel
Algorithms
spellingShingle Ciencias Informáticas
evolutionary-statistical system
uncertainty reduction problems
wildfires
Distributed
Parallel
Algorithms
BIanchini, Germán
Méndez Garabetti, Miguel
Caymes Scutari, Paola
Evolutionary-statistical system for uncertainty reduction problems in wildfires
topic_facet Ciencias Informáticas
evolutionary-statistical system
uncertainty reduction problems
wildfires
Distributed
Parallel
Algorithms
description Fire modelling is used by engineers and scientists to understand and to predict possible fire behaviour. Empirical, semi-empirical, and physical models have been developed to predict wildfire behaviour. Any of these can be used to develop simulators and tools for preventing and fighting wildfires. However, in many cases the models present a series of limitations related to the need for a large number of input parameters. Moreover, such parameters often have some degree of uncertainty due to the impossibility of getting all of them in real time. Consequently, these values have to be estimated from indirect measurements, which negatively impacts on the output of the model. In this paper we show a method which takes advantage of the computational power provided by High Performance Computing to improve the quality of the output of the model. This method combines Statistical Analysis with Parallel Evolutionary Algorithms. Besides, we compare this method with a previous version which did not use evolutionary algorithms.
format Objeto de conferencia
Objeto de conferencia
author BIanchini, Germán
Méndez Garabetti, Miguel
Caymes Scutari, Paola
author_facet BIanchini, Germán
Méndez Garabetti, Miguel
Caymes Scutari, Paola
author_sort BIanchini, Germán
title Evolutionary-statistical system for uncertainty reduction problems in wildfires
title_short Evolutionary-statistical system for uncertainty reduction problems in wildfires
title_full Evolutionary-statistical system for uncertainty reduction problems in wildfires
title_fullStr Evolutionary-statistical system for uncertainty reduction problems in wildfires
title_full_unstemmed Evolutionary-statistical system for uncertainty reduction problems in wildfires
title_sort evolutionary-statistical system for uncertainty reduction problems in wildfires
publishDate 2012
url http://sedici.unlp.edu.ar/handle/10915/23618
work_keys_str_mv AT bianchinigerman evolutionarystatisticalsystemforuncertaintyreductionproblemsinwildfires
AT mendezgarabettimiguel evolutionarystatisticalsystemforuncertaintyreductionproblemsinwildfires
AT caymesscutaripaola evolutionarystatisticalsystemforuncertaintyreductionproblemsinwildfires
bdutipo_str Repositorios
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