Capturing Urban Pluvial River Flooding Features Based on the Fusion of Physically Based and Data-Driven Approaches

Driven by climate change and rapid urbanization, pluvial flooding is increasingly endangering urban environments, prompting the widespread use of coupled hydrological-hydrodynamic models that enable more accurate urban flood simulations and enhanced pluvial flood forecasting. The simulation method for urban river flooding caused by heavy rainfall has garnered growing attention. However, existing studies primarily concentrate on prediction using hydrodynamic models or machine learning models, and there remains a dearth of a comprehensive prediction framework that couples both models to simulate the temporal evolution of river flood changes. This research proposes a novel framework for simulating urban pluvial river flooding by integrating physically based models with deep learning approaches. The sample set is enhanced through data augmentation and Generative Adversarial Networks, and scheduling control signals are incorporated into the encoder-decoder architecture to enable urban pluvial river flooding forecasting. The results demonstrate strong model performance, provided that the model's structural complexity is aligned with the available training data. After incorporating scheduling information, the simulated water level process exhibits a "double-peak" pattern, where the first peak is noticeably lower than that under non-scheduling conditions. The current research introduces an innovative method for simulating and analyzing large-scale urban flooding, offering valuable perspectives for urban planning and flood mitigation strategies.