Web of Science: 4 cites, Scopus: 4 cites, Google Scholar: cites
Applying probability theory for the quality assessment of a wildfire spread prediction framework based on genetic algorithms
Cencerrado Barraqué, Andrés (Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius)
Cortés Fité, Ana (Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius)
Margalef, Tomàs (Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius)

Data: 2013
Resum: This work presents a framework for assessing how the existing constraints at the time of attending an ongoing forest fire affect simulation results, both in terms of quality (accuracy) obtained and the time needed to make a decision. In the wildfire spread simulation and prediction area, it is essential to properly exploit the computational power offered by new computing advances. For this purpose, we rely on a two-stage prediction process to enhance the quality of traditional predictions, taking advantage of parallel computing. This strategy is based on an adjustment stage which is carried out by a well-known evolutionary technique: Genetic Algorithms. The core of this framework is evaluated according to the probability theory principles. Thus, a strong statistical study is presented and oriented towards the characterization of such an adjustment technique in order to help the operation managers deal with the two aspects previously mentioned: time and quality. The experimental work in this paper is based on a region in Spain which is one of the most prone to forest fires: El Cap de Creus.
Ajuts: Ministerio de Ciencia e Innovación TIN2011-28689-C02-01
Ministerio de Ciencia e Innovación TIN2007-64974
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Publicat a: The Scientific World Journal, Vol. 2013 (November 2013) , art. 728414, ISSN 1537-744X

DOI: 10.1155/2013/728414
PMID: 24453898


13 p, 3.1 MB

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