Enhancing disaster prediction with Bayesian deep learning: a robust approach for uncertainty estimation

Abstract

Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods generally lack uncertainty estimation capabilities, limiting their practical effectiveness in high-risk scenarios. To overcome these limitations, this study proposes an enhanced Bayesian Deep Neural Network (BDNN) tailored for flood forecasting, effectively integrating Variational Inference (VI), Monte Carlo (MC) Dropout, and a Hierarchical Attention Mechanism. By leveraging hydrological and meteorological data from the Yellow River basin (2001–2023), the BDNN model not only achieves superior prediction accuracy (94.6%) but also significantly enhances reliability through robust uncertainty quantification. Comparative analyses demonstrate that the proposed approach markedly outperforms conventional models such as Random Forest, XGBoost, and Multi-layer Perceptron. Ablation studies further confirm the critical role of the hierarchical attention mechanism in capturing essential features, while VI and MC Dropout substantially improve prediction reliability. These advancements highlight the potential of BDNNs to significantly enhance disaster preparedness and support more informed emergency response decisions in complex, uncertain environments.