Efficient drainage network control: from hydrodynamic modelling to AI-supported decision-making

Climate change is causing an increase in extreme, high-intensity rainfall events, which are locally and temporally limited and pose a significant risk potential for urban stormwater drainage. Particularly affected are densely populated and urban hardscapes, where substantial damage potential is expected. The hydraulics of the drainage network can be calculated with a high degree of accuracy using spatially and temporally high-resolution rainfall data. Numerical stormwater models are used for this purpose. However, such models have the disadvantage of long computation times, which can exceed the time scale of a forecast depending on the application. Our aim is to improve the forecasting and early warning systems for the operational optimisation of drainage network control and hazard prevention in the stormwater drainage system using artificial neural networks (ANN).

The study area is a part of the city of Osnabrück in Germany. To quantify the risk of overflow, a hydrodynamic stormwater model with 1896 real and synthetic rainfall events with a temporal resolution of 5 minutes, durations between 15 and 60 minutes and return periods between 1 and 100 years is operated. The catchment area is divided into 1x1km pixels and one of four risk categories is defined for each pixel based on the sum of the overflow for each time step. In order to check whether the risk categories can be reduced by controlling the drainage network, three different control scenarios of the drainage network are calculated hydrodynamically in addition to the uncontrolled state, so that 7584 (4 x 1896) simulations are available for training the ANN. Finally, the ANN will be evaluated for its suitability to support decision-making for an optimised control scenario in real time. A particular challenge here is the evaluation of the AI model in the comparison of the risk categories in neighbouring pixels.