A deep learning model for real-time forecasting of 2-D river flood inundation maps

Abstract. Floods are among the most hazardous natural disasters worldwide. Accurate and rapid flood predictions are critical for effective early warning systems and flood management strategies. The high computational cost of hydrodynamic models often limits their application in real-time flood simulations. Conversely, data-driven models are gaining attention due to their high computational efficiency. In this study, we aim at assessing the effectiveness of transformer-based models for forecasting the spatiotemporal evolution of fluvial floods in real-time. To this end, the transformer-based data-driven model FloodSformer (FS) has been adapted to predict river flood inundations with negligible computational time. The FS model leverages an autoencoder framework to analyze and reduce the dimensionality of spatial information in input water depth maps, while a transformer architecture captures spatiotemporal correlations between inundation maps and inflow discharges using a cross-attention mechanism. The trained model can predict long-lasting events using an autoregressive procedure. The model's performance was evaluated in two case studies: an urban flash flood scenario at the laboratory scale and a river flood scenario along a segment of the Po River (Italy). Datasets were numerically generated using a two-dimensional hydrodynamic model. Special attention was given to analyzing how the accuracy of predictions is influenced by the type and severity of flood events used to create the training dataset. The results show that prediction errors generally align with the uncertainty observed in physically based models, and that larger and more diverse training datasets help improving the model's accuracy. Additionally, the computational time of the real-time forecasting procedure is negligible compared to the physical time of the simulated event. The performance of the FS model was also benchmarked against a state-of-the-art convolutional neural network architecture and showed better accuracy. These findings highlight the potential of transformer-based models in enhancing flood prediction accuracy and responsiveness, contributing to improve flood management and resilience.