Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning

TL;DR

This paper introduces a physics-guided machine learning surrogate model that significantly accelerates hydrodynamic simulations of urban drainage systems while maintaining high accuracy, enabling real-time analysis and detailed network-level insights.

Contribution

The paper presents a novel physics-guided machine learning approach for surrogate modeling of urban drainage hydraulics, achieving rapid simulations with detailed network-level outputs.

Findings

Simulation times reduced by 10-100x compared to HiFi models.

Achieved R2 values around 0.9 for time series accuracy.

Training time for surrogates is approximately one hour.

Abstract

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.