Real-Time Dynamic Urban Flood Prediction: An Integrated Deep Learning Architecture with Adaptive Updating

This study develops an adaptive urban flood forecasting framework by integrating hydrodynamic modeling and deep learning. A coupled hydraulic model is first used to generate physically consistent training data that jointly represent subsurface network dynamics and surface inundation processes. Based on these data, the framework combines a Crossformer model for forecasting global nodal water levels, an autoencoder for compressing high-dimensional inundation fields, and a multilayer perceptron that maps subsurface hydraulic states to the latent features of surface flooding. To support operation under sparse monitoring conditions, inversion models are introduced to reconstruct global 1D and 2D flood states from limited sensor observations, and an online continual learning strategy is used to update the forecasting model during deployment. Results show that the proposed framework achieves high predictive skill for both subsurface and surface flooding, with NSE values above 0.97 for nodal water levels and CSI values above 0.93 for inundation extent, while the online updating strategy reduces RMSE by about 40% during extreme rainfall events. These results demonstrate that the proposed framework provides an effective and operationally practical route toward adaptive urban flood forecasting under coupled sewer–surface conditions and sparse monitoring constraints.