Modeling Wildfire Initial Attack Success Rate Based on Machine Learning in Liangshan, China

Abstract

The initial attack is a critical phase in firefighting efforts, where the first batch of resources are deployed to prevent the spread of the fire. This study aimed to analyze and understand the factors that impact the success of the initial attack, and used three machine learning models—logistic regression, XGBoost, and artificial neural network—to simulate the success rate of the initial attack in a specific region. The performance of each machine learning model was evaluated based on accuracy, AUC (Area Under the Curve), and F1 Score, with the results showing that the XGBoost model performed the best. In addition, the study also considered the impact of weather conditions on the initial attack success rate by dividing the scenario into normal weather and extreme weather conditions. This information can be useful for forest fire managers as they plan resource allocation, with the goal of improving the success rate of the initial attack in the area.