A Forest Fire Risk Prediction Framework Based on Machine Learning Models in the Greater Khingan

The Greater Khingan, a key cold-temperate coniferous forest region in northern China, is frequently affected by forest fires with severe ecological and economic impacts. The study investigates the influence of key environmental and anthropogenic drivers on forest fire susceptibility and evaluates multiple machine-learning approaches for regional fire assessment. Using 2001–2018 fire point data and multi-source remote sensing data, we integrated 13 driving factors across four dimensions: meteorology, topography, vegetation, and human activities. Collinear variables were screened using the Variance Inflation Factor (VIF). Three machine learning models—Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—were constructed to assess the long-term potential risk of forest fire occurrence. Driving mechanisms were analyzed using standardized regression coefficients and the SHapley Additive exPlanations (SHAP) interpretable algorithm, and spatial distribution maps of regional forest fire risk were generated based on the optimal model. Among the three models, RF achieved the highest predictive accuracy, with an accuracy of 0.919 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.966, significantly outperforming LR and SVM. SHAP analysis reveals that forest fires are primarily driven by climatic factors (Pres and Prec as core drivers), regulated by topographic factors, and weakly affected by human factors. The proposed framework provides an effective tool for long-term forest fire susceptibility assessment by combining robust predictive performance with interpretable model outputs. The findings provide scientific support for long-term strategic forest fire risk zoning, regional firefighting resource allocation, and the formulation of differentiated prevention and control strategies, and also offer methodological references for forest fire prediction in other cold-temperate forest regions in China.