info:eu-repo/semantics/article
Hybrid-parallel uncertainty reduction method applied to forest fire spread prediction
Fecha
2017-04Registro en:
Mendez Garabetti, Miguel; Bianchini, German; Tardivo, María Laura; Caymes Scutari, Paola Guadalupe; Gil Costa, Graciela Verónica; Hybrid-parallel uncertainty reduction method applied to forest fire spread prediction; Ibero-American Science and Technology Education Consortium; Journal of Computer Science & Technology; 17; 1; 4-2017; 12-19
1666-6046
CONICET Digital
CONICET
Autor
Mendez Garabetti, Miguel
Bianchini, German
Tardivo, María Laura
Caymes Scutari, Paola Guadalupe
Gil Costa, Graciela Verónica
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.