Emulation of a Detailed Urban Drainage Simulator to Be Applied for Short-Term Predictions

The challenge of this study is to investigate on applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a surrogate model for a computationally expensive and detailed urban drainage simulator. The novelty is the consideration of (short) time series for the simulation inputs and outputs. Such simulation setup is interesting in applications such as Model Predictive Control (MPC) in which numerous, fast and frequent simulation results are required. Here, an emulator is developed to predict a storage tank’s volume in a small case study in Luxembourg. Three main inputs are considered as the GPE’s parameters: Initial volume in the tank, the level in which the outlet pump of the tank must start to work, and the time series of expected rainfall in the upcoming 2 h. The output of interest is the total volume of the storage tank for the next 24 h. A dataset of 2000 input-output scenarios were produced using different possible combinations of the inputs and running the detailed simulator (InfoWorks® ICM). 80% of the dataset were applied to train the emulator and 20% to validate the results. Distributions of Nash-Sutcliffe efficiency and Volumetric Efficiency are presented as indicators for quantification of the emulation error. Based on the preliminary results, it can be concluded that the introduced technique is able to reduce the simulations runtime significantly while imposing some inevitable accuracy cost. More investigation is required to validate the more generic applicability of this technique for multiple outputs and interactions between different urban drainage components.