UCLA

This study aims to analyze the dynamics of wildfires in the United States by predicting fire size for both small and large fires and developing a classification model for fire size. Using a dataset spanning from 1992 to 2015, machine learning models such as XGBoost, CatBoost, Random Forest, Generalized Linear Models (GLMs), and Mult-layer Perceptron (MLP) were applied to predict fire size, with XGBoost and CatBoost showing strong performance in predicting small and large fires, respectively. Additionally, classification models, including XGBoost, CatBoost, and Random Forest, were developed to distinguish between small and large fires, with challenges arising from class imbalance. Future work will focus on improving model performance by incorporating more detailed environmental data and exploring advanced machine learning techniques.