Predictive modeling of river blockage severity from debris flows: Integrating statistical and machine learning approaches with insights from Sichuan Province, China

Abstract

River blockages caused by debris flows pose serious threats to the environment and infrastructure. This study introduces the river blockage index (RBI) as a key measure of river blockage severity. We collected data from 60 debris flow events to build a comprehensive dataset. To enhance model robustness and accuracy, we optimized variable selection using multicollinearity analysis and the Akaike information criterion (AIC). Four statistical models were developed, including multiple linear, logarithmic, power, and exponential regressions. We also constructed models based on machine learning algorithms, including random forests and gradient boosting decision trees, and tested them using 5-fold cross-validation. After confirming that the training dataset met linear statistical assumptions, we built robust regression models. We tested the significance of the regression equations and coefficients using F-tests and t-tests. Hyperparameters of the machine learning algorithms were optimized through Bayesian methods. Model performance was evaluated using metrics such as R², adjusted R², mean absolute error (MAE), mean relative error (MRE), and root mean square error (RMSE). Results show that the most important factors influencing RBI are catchment area (A) and the discharge ratio between the debris flow and the main river (Q). Among the statistical models, the logarithmic and power models performed best due to their simplicity and efficiency. The random forest model demonstrated the highest predictive accuracy and stability overall. By combining statistical methods with machine learning, we improved prediction accuracy and provided practical guidance for disaster prevention strategies. This approach overcomes the limitations of numerical simulations and experimental studies, offering a more flexible and efficient method for RBI prediction. Future work will extend these findings to other geological settings to further enhance model adaptability and performance.