# Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks

arXiv: https://arxiv.org/abs/2404.10324 · HTML: https://mlsanity.com/paper/2404.10324/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features](https://mlsanity.com/paper/2111.08450/) (2021)
- [Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding](https://mlsanity.com/paper/2104.02276/) (2021)
- [Reconstructing nodal pressures in water distribution systems with graph neural networks](https://mlsanity.com/paper/2104.13619/) (2021)
- [Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning](https://mlsanity.com/paper/2206.01538/) (2022)
- [pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms](https://mlsanity.com/paper/2110.12289/) (2021)

## Built on by (most cited first, 2 of 2)

- [DUALFloodGNN: Physics-informed Graph Neural Network for Operational Flood Modeling](https://mlsanity.com/paper/2512.23964/) (2025)
- [Flow Reconstruction from Sparse Measurements in Urban Drainage Networks: An Application and Evaluation of Data-Driven Sparse Sensing](https://mlsanity.com/paper/2511.04556/) (2025)

## Abstract (arXiv)

Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potential surrogate models, but may suffer from low interpretability and efficiency in fitting complex targets. Owing to the state-of-the-art modelling power of graph neural networks (GNNs) and their match with urban drainage networks in the graph structure, this work proposes a GNN-based surrogate of the flow routing model for the hydraulic prediction problem of drainage networks, which regards recent hydraulic states as initial conditions, and future runoff and control policy as boundary conditions. To incorporate hydraulic constraints and physical relationships into drainage modelling, physics-guided mechanisms are designed on top of the surrogate model to restrict the prediction variables with flow balance and flooding occurrence constraints. According to case results in a stormwater network, the GNN-based model is more cost-effective with better hydraulic prediction accuracy than the NN-based model after equal training epochs, and the designed mechanisms further limit prediction errors with interpretable domain knowledge. As the model structure adheres to the flow routing mechanisms and hydraulic constraints in urban drainage networks, it provides an interpretable and effective solution for data-driven surrogate modelling. Simultaneously, the surrogate model accelerates the predictive modelling of urban drainage networks for real-time use compared with the physics-based model.