End-to-End Graph Neural Networks for Real-Time Hydraulic Prediction in Stormwater Systems

Abstract. Urban stormwater systems (SWS) play a critical role in protecting communities from pluvial flooding, ensuring public safety, and supporting resilient infrastructure planning. As climate variability intensifies and urbanization accelerates, there is a growing need for timely and accurate hydraulic predictions to support real-time control and flood mitigation strategies. While physics-based models such as SWMM provide detailed simulations of rainfall-runoff and flow routing processes, their computational demands often limit their feasibility for real-time applications. Surrogate models based on machine learning offer faster alternatives, but most rely on fully connected or grid-based architectures that struggle to capture the irregular spatial structure of drainage networks, often requiring precomputed runoff inputs and focusing only on node-level predictions. To address these limitations, we present GNN-SWS, a novel end-to-end graph neural network (GNN) surrogate model that emulates rainfall-driven hydraulic behavior across stormwater systems. The model predicts hydraulic states at both junctions and conduits directly from rainfall inputs, capturing the coupled dynamics of runoff generation and flow routing. It incorporates a spatiotemporal encoder–processor–decoder architecture with tailored message passing, autoregressive forecasting, and physics-guided constraints to improve predictive accuracy and physical consistency. Additionally, a training strategy based on the pushforward trick enhances model stability over extended prediction horizons. Applied to a real-world urban watershed, GNN-SWS demonstrates strong potential as a fast, scalable, and data-efficient alternative to traditional solvers. This framework supports key applications in urban flood risk assessment, real-time stormwater control, and the optimization of resilient infrastructure systems.