Fuel Load Models for Different Tree Vegetation Types in Sichuan Province Based on Machine Learning

(1) Objective: To improve forest fire prevention, this study provides a reference for forest fire risk assessment in Sichuan Province. (2) Methods: This research focuses on various forest vegetation types in Sichuan Province. Given data from 6848 sample plots, five machine learning models—random forest, extreme gradient boosting (XGBoost), k-nearest neighbors, support vector machine, and stacking ensemble (Stacking)—were employed. Bayesian optimization was utilized for hyperparameter tuning, resulting in machine learning models for predicting forest fuel loads (FLs) across five different vegetation types. (3) Results: The FL model incorporates not only vegetation characteristics but also site conditions and climate data. Feature importance analysis indicated that structural factors (e.g., canopy closure, diameter at breast height, and tree height) dominated in cold broadleaf, subtropical broadleaf, and subtropical mixed forests, while climate factors (e.g., mean annual temperature and temperature seasonality) were more influential in cold coniferous and subtropical coniferous forests. Machine learning-based FL models outperform the multiple stepwise regression model in both fitting ability and prediction accuracy. The XGBoost model performed best for cold coniferous, cold broadleaf, subtropical broadleaf, and subtropical mixed forests, with coefficient of determination (R2) values of 0.79, 0.85, 0.81, and 0.83, respectively. The Stacking model excelled in subtropical coniferous forests, achieving an R2 value of 0.82. (4) Conclusions: This study establishes a theoretical foundation for predicting forest fuel capacity in Sichuan Province. It is recommended that the XGBoost model be applied to predict fuel loads (FLs) in cold coniferous forests, cold broadleaf forests, subtropical broadleaf forests, and subtropical mixed forests, while the Stacking model is suggested for predicting FLs in subtropical coniferous forests. Furthermore, this research offers theoretical support for forest fuel management, forest fire risk assessment, and forest fire prevention and control in Sichuan Province.