Seasonal forest fire risk and key drivers in Yunnan Province: A machine learning approach

The objective of this paper is to develop and evaluate forest fire prediction models using machine learning techniques, with a focus on identifying the most effective model and the key driving factors influencing fire occurrence across different seasons in Yunnan Province, China.

The results indicated that the BRT was the best model for predicting forest fires, and meteorological and human factors were the important driving factors for fire occurrence. The XGBoost was the optimal model for predicting fires in summer and autumn, mainly influenced by meteorological and soil vegetation factors.