FIRE BEHAVIOR PREDICTION USING MACHINE LEARNING ALGORITHMS

Vinícius Barros Rodrigues,Fillpe Tamiozzo Pereira Torres

doi:10.28951/rbb.v38i3.452

Vinícius Barros Rodrigues, Fillpe Tamiozzo Pereira Torres

Open Access

https://doi.org/10.28951/rbb.v38i3.452

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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.

Highlights

Wildfires frequency and their impacts are increasing (SAN-MIGUEL-AYANZ et al, 2012)
We assessed the performance of two machine learning algorithms (GLMNET and Random Forest) applied for modelling and predicting fire behavior in an Eucalyptus plantation located in Viçosa city, Minas Gerais state, Brazil
The variables "Days without rain", "Air temperature" and "Bed depth" was not significant in the “Rate of spread model” (Figure 3f). This is the first approach based on Machine Learning to modeling fire behavior

Summary

Introduction

Wildfires frequency and their impacts are increasing (SAN-MIGUEL-AYANZ et al, 2012) They can change ecosystem structures and threaten human lives, which makes fire behavior and wildfire prediction crucial issues to mitigate fire impacts (BOWMAN et al, 2009). In this context, persons in public management must decide the most effective distribution in scenarios with a limited amount of resources (MAVSAR et al, 2013). According to Rodrigues and de la Riva (2014), the ML models have shown good predictive accuracy applied for other disciplines. Those models usually show good generalization abilities, even when modelling high dimensional and complex nonlinear phenomena (HASTIE et al, 2009)

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