We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a limited set of simulation results from a hydrodynamic (HiFi) model. Our approach reduces simulation times by one to two orders of magnitude compared to a HiFi model. It is thus slower than e.g. conceptual hydrological models, but it enables simulations of water levels, flows and surcharges in all nodes and links of a drainage network and thus largely preserves the level of detail provided by HiFi models. Comparing time series simulated by the surrogate and the HiFi model, R² values in the order of 0.9 are achieved. Surrogate training times are currently in the order of one hour. However, they can likely be reduced through the application of transfer learning and graph neural networks. Our surrogate approach will be useful for interactive workshops in initial design phases of urban drainage systems, as well as for real time applications. In addition, our model formulation is generic and future research should investigate its application for simulating other water systems.

我们提出并展示了一种基于物理引导机器学习的快速准确的城市排水系统水力学替代建模新方法。针对来自流体动力学 (HiFi) 模型的一组有限的模拟结果对代理进行训练。与 HiFi 模型相比，我们的方法将仿真时间减少了一到两个数量级。因此，它比概念水文模型慢，但它能够模拟排水管网的所有节点和链接中的水位、流量和附加费，因此在很大程度上保留了 HiFi 模型提供的详细程度。比较代理和 HiFi 模型模拟的时间序列，R ²达到 0.9 左右的值。代孕培训时间目前约为一小时。然而，它们可能会通过迁移学习和图神经网络的应用来减少。我们的替代方法将有助于城市排水系统初始设计阶段的互动研讨会以及实时应用。此外，我们的模型公式是通用的，未来的研究应该研究其在模拟其他水系统中的应用。