Machine learning for wildfire classification: Exploring blackbox, eXplainable, symbolic, and SMOTE methods

Abstract

Whether triggered by natural or human-made events, wildfires are considered one of the most traumatic events to our community and environment. Thus, properly predicting wildfires continues to be an active area of research. This work showcases a statistical overview of the problem of wildfires and then presents a dense data-driven (D3) approach that leverages a variety of machine learning (ML) techniques, namely, blackbox and eXplainable ML (i.e., deep learning (DL), decision tree (DT), Stochastic Gradient Descent (SGD), Extreme Gradient Boosted Trees (ExGBT), Logistic regression (LR)), and symbolic ML via genetic algorithms (GA) to classify and predict wildfire breakouts. This approach was developed and validated using two databases comprising more than 1.04 million geo-referenced wildfires that burned over 359,000 ​km2 (88.7 million acres) between 1992 and 2015 in North America and Europe. Despite the complex nature of wildfire formation and the interdependency of its governing factors, the findings of this D3 analysis show the feasibility of utilizing ML in preciously classifying the expected size of wildfires and predicting the possibility of the breakout of wildfires.