A highly generalizable data-driven model for spatiotemporal urban flood dynamics real-time forecasting based on coupled CNN and ConvLSTM

Flooding has become one of the most severe natural hazards in urban areas. Real-time and accurate prediction of flood processes is a crucial approach to mitigate urban flood disasters. Data-driven models based on machine learning methods offer significantly higher computational efficiency than physics-based models and have been widely applied in real-time urban flood simulation. However, most data-driven models target the temporal process of inundation depths at specific sites or the spatial distribution of peak inundation depths, while some models capable of simulating spatiotemporal urban flood inundation often lack spatial generalization capabilities. In this study, we proposed a novel data-driven model to predict the spatiotemporal distribution dynamics of urban inundation depths. The model integrates a ConvLSTM-based component alongside a CNN-based component via a concatenation process, facilitating the extraction of information from both temporal sequences and static geospatial features concurrently. A tiling approach that divides the study area into distinct spatial sub-regions, which serve as independent training samples, was employed during model training to enhance the model's generalization capability. The proposed model was applied to a flood-prone urban area in Macao and compared with a physics-based model. The results show that: (1) the proposed model effectively captures the inundation processes at specific sites, with NSE > 0.80 for the majority events, as well as RMSE and MAE values < 0.20. (2) The proposed data-driven model demonstrates robust generalization performance, with simulated inundation processes closely aligned with the results of the physics-based model in most regions (mean NSE > 0.70, RMSE < 0.10, MAE < 0.10). Notable discrepancies persist only in localized zones of abrupt terrain variations, particularly near building edges.