This study integrates multi-source driving factors from 2005 to 2024, including meteorological elements, fire danger indices, vegetation, topography, and human activities, to comparatively analyze the performance of four machine learning models—RF, LightGBM, XGBoost, and DNN—in the daily scale prediction of forest and grassland fires in Sichuan Province. A Nested Cross-Validation framework, using year as the grouping variable, was employed to evaluate model robustness, and the SHAP method was introduced to quantify the driving mechanisms of the factors. The results indicate that LightGBM is the overall optimal model, with its ROC AUC reaching 0.9193 in nested cross-validation and 0.9411 in independent temporal testing, demonstrating superior predictive capability and cross-year generalization performance. SHAP analysis reveals a hierarchical structure of fire drivers: land type constitutes the a priori physical constraint for fire occurrence, meteorological and drought indicators dominate the differentiation of the risk gradient, while topography and human activities serve as spatial modulators. In an independent sample validation on 9 December 2024, high-fire-risk grids showed high consistency with the distribution of actual fire points. For transmission line application scenarios, a risk distribution map constructed by coupling fire risk values with wind speed thresholds successfully identified actual fire point areas, indicating that this framework can provide a scientific basis for forest and grassland fire prevention and for power grid enterprises to conduct precise early warning and regionalized control.
Wáng et al. (2026) studied this question.