Heterogeneous Spatio-Temporal Series Forecasting Using Dynamic Graph Neural Networks for Flood Prediction

Accurate flood prediction is essential for disaster mitigation, life protection, and minimizing community and infrastructure impact. However, current flood prediction models often struggle with challenges such as capturing intricate spatiotemporal dynamics, adapting to changing environmental conditions, and integrating diverse variables effectively. In this paper, we propose a novel approach for flood forecasting, say a Heterogeneous Dynamic Temporal Graph Convolutional Network (HD-TGCN). The Dynamic Temporal Graph Convolution Module (D-TGCM) adapts to evolving temporal graph structures, utilizing a Multi-Head Self-Attention mechanism to generate adjacency matrices dynamically. This enhances the adaptability of the model to changing temporal graph structures and captures intricate dependencies among features at different time steps. Additionally, HD-TGCN incorporates parallel DTGCMs to address the heterogeneity in flood spatiotemporal data. This module effectively captures intricate relationships among diverse variable types, improving the model's ability to handle complex multivariable interactions and enabling the model to process heterogeneous graphs effectively. Experiments on real-world datasets demonstrate that our model outperforms state-of-the-art models. At a prediction horizon of 30 minutes, the model exhibits improvements of 78.02%, 66.30%, and 0.15% in terms of MAE, RMSE, and NSE, respectively.