Conference Agenda

High-fidelity hydrodynamic models, such as the Storm Water Management Model (SWMM), provide accurate simulations of urban drainage systems but are computationally expensive. Graph Neural Networks (GNNs) have emerged as promising metamodels to approximate SWMM behaviour efficiently. However, the impact of GNN depth on predictive performance remains underexplored. This study investigates how increasing graph layer depth influences the accuracy of a GNN metamodel for a sloping urban drainage system.

Using an auto-regressive GNN framework, we evaluated multiple model depths, ranging from shallow to deep architectures, on a calibrated SWMM case study in Loenen, Netherlands. Results show that shallow GNNs struggle to capture transport-dominated hydraulic dynamics, leading to poor performance. In contrast, deeper models more accurately approximate SWMM’s hydrodynamic responses, achieving RMSE reductions from 10 cm to 5 cm. Contrary to conventional GNN literature, which suggests diminishing returns with depth due to oversmoothing, our findings indicate improvement with metamodels of up to 12 layers. However, deeper architectures impose higher computational costs.

These findings emphasize the importance of depth as a key hyperparameter in GNN-based metamodeling for urban drainage applications. Future work should focus on optimizing computational efficiency through network skeletonization, improving the explainability of hyperparameters and attention weights, and hardware acceleration.