Fire behavior prediction using machine learning algorithms

Abstract

Wildfires can affect ecosystem structure and threaten human lives. Understanding fire behavior and predicting fire activities is a crucial issue to mitigate fire impacts. Machine Learning is currently an important tool for the modeling, analysis, and visualization of environmental data and wildfire events. In this study, we assessed the performance of two machine learning algorithms for modeling and predicting fire intensity, the height of flames, and fire rate of spreading in Eucalyptus urophylla (Myrtaceae, Myrtales) and Eucalyptus grandis (Myrtaceae, Myrtales) plantations spatially located in Viçosa-MG, Brazil. The Random Forest showed to be the best algorithm for fire modeling, with climatic conditions, and moisture of the combustible material being the variables that significantly affect the prediction of fire behavior.