Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction

• Miguel Méndez-Garabetti ^[1] ; Germán Bianchini ^[1] ; María Laura Tardivo ^[1] ; Paola Caymes-Scutari ^[1] ; Verónica Gil-Costa ^[2]
  1. [1] Universidad Tecnológica Nacional

     Universidad Tecnológica Nacional

     Argentina

     
  2. [2] Universidad Nacional de San Luis

     Universidad Nacional de San Luis

     Argentina

     
• Localización: Journal of Computer Science and Technology, ISSN-e 1666-6038, Vol. 17, Nº. 1, 2017, págs. 12-19
• Idioma: inglés
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• Resumen

  • Fire behavior prediction can be a fundamental tool to reduce losses and damages in emergency situations. However, this process is often complex and affected by the existence of uncertainty. For this reason, from different areas of science, several methods and systems are developed and refined to reduce the effects of uncertainty In this paper we present the Hybrid Evolutionary-Statistical System with Island Model (HESS-IM). It is a hybrid uncertainty reduction method applied to forest fire spread prediction that combines the advantages of two evolutionary population metaheuristics: Evolutionary Algorithms and Differential Evolution. We evaluate the HESS-IM with three controlled fires scenarios, and we obtained favorable results compared to the previous methods in the literature.