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

Journal of Computer Science and Technology

卷:17

Miguel Méndez Garabetti¹  Graciela Verónica Gil Costa¹  María Laura Tardivo²  Paola Caymes Scutari³  Germán BIanchini³ 

[1] Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.;

[2] Departamento de Computación, Facultad de Ciencias Exactas, Físico-Químicas y Naturales, Universidad Nacional de Córdoba, Córdoba, Argentina;

[3] Laboratorio de Investigación en Cómputo Paralelo/Distribuido (LICPaD), Departamento de Ingeniería en Sistemas de Información, Facultad Regional Mendoza - Universidad Tecnológica Nacional.Mendoza, Argentina.;

关键词: hybrid metaheuristics;    differential evolution;    evolutionary algorithms;    fire prediction;    uncertainty reduction;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown