A Hybrid Approach to Mountain Torrent-Induced Debris Flow Prediction Combining Experiments and Gradient Boosting Regression

Abstract

Debris flows are highly unpredictable and destructive natural hazards that present significant risks to both human life and infrastructure. Despite advances in machine learning techniques, current predictive models often fall short due to the insufficient and low-quality historical data available for training. In this study, we introduce a hybrid approach that combines physical model experiments with a gradient boosting regression model to enhance the accuracy and reliability of debris flow predictions. By integrating experimental data that closely simulate real-world flow conditions, the gradient boosting regression model is trained on a more robust foundation, enabling it to capture the complex dynamics of debris flows under various conditions. Selecting slide mass, slope length, yield stress, and slope angle as explanatory variables, we focus on quantify two critical debris flow parameters—frontal thickness and velocity—at indicated locations within the flow. The model demonstrates strong predictive performance in forecasting these key parameters, achieving coefficients of determination of 0.938 for slide thickness and 0.934 for slide velocity. This hybrid approach not only simplifies the prediction process but also significantly improves its precision, offering a valuable tool for real-time risk assessment and mitigation strategies in debris flow-prone regions.