A Novel Bayesian Probabilistic Approach for Debris Flow Accumulation Volume Prediction Using Bayesian Neural Networks with Synthetic and Real-World Data Integration

Debris flow events are complex natural phenomena that are challenging to predict, especially when data are limited or uncertain. This study presents a novel probabilistic approach using Bayesian Neural Networks (BNN) to predict possible volumes of debris flow accumulation by using synthetic and real-world data. Synthetic datasets are created based on statistical distributions informed by geomorphological and hydrological knowledge, allowing the model to learn typical behaviors when real data is scarce. BNN provide uncertainty quantification by modeling neural weights as probability distributions. The model resulting from validation on synthetic data and two real datasets from China and South Korea show strong predictive performance (R2 > 0.98) and close alignment between predicted and observed volumes, even in the presence of outliers. The key strength of this integrated approach lies in its integration of synthetic data generation, real data augmentation based on Bootstrapping, expert knowledge and Bayesian deep learning to overcome limitations of traditional statistical models, improving debris flow forecasting and enabling more informed and resilient risk management strategies.