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A Data-Driven Surrogate Modelling Approach for Acceleration of Short-Term Simulations of a Dynamic Urban Drainage Simulator

1
Environmental Informatics Group, ERIN Department, Luxembourg Institute of Science and Technology (LIST), L-4422 Belvaux, Luxembourg
2
Sanitary Engineering Section, Water Management Department, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands
3
Soil Geography and Landscape Department, Wageningen University, 6700 AA Wageningen, The Netherlands
4
RTC4Water, L-4362 Belval, Luxembourg
5
Hydraulic Engineering Department, Deltares, 2600 MH Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Water 2018, 10(12), 1849; https://doi.org/10.3390/w10121849
Received: 22 November 2018 / Revised: 4 December 2018 / Accepted: 6 December 2018 / Published: 13 December 2018
(This article belongs to the Special Issue Design of Urban Water Drainage Systems)
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Abstract

In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving force is a challenge in this regard due to the high dimensionality problem. However, this problem can be less relevant when the focus is only on short-term simulations. The novelty of this research is the consideration of short-term rainfall time series as training parameters for the GPE. Rainfall intensity at each time step is counted as a separate parameter. A method to generate synthetic rainfall events for GPE training purposes is introduced as well. Here, an emulator is developed to predict the upcoming daily time series of the total wastewater volume in a storage tank and the corresponding Combined Sewer Overflow (CSO) volume. Nash-Sutcliffe Efficiency (NSE) and Volumetric Efficiency (VE) are calculated as emulation error indicators. For the case study herein, the emulator is able to speed up the simulations up to 380 times with a low accuracy cost for prediction of the total storage tank volume (medians of NSE = 0.96 and VE = 0.87). CSO events occurrence is detected in 82% of the cases, although with some considerable accuracy cost (medians of NSE = 0.76 and VE = 0.5). Applicability of the emulator for consecutive short-term simulations, based on real observed rainfall time series is also validated with a high accuracy (NSE = 0.97, VE = 0.89). View Full-Text
Keywords: surrogate model; data-driven; Gaussian process; emulator; urban drainage surrogate model; data-driven; Gaussian process; emulator; urban drainage
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Mahmoodian, M.; Torres-Matallana, J.A.; Leopold, U.; Schutz, G.; Clemens, F.H.L.R. A Data-Driven Surrogate Modelling Approach for Acceleration of Short-Term Simulations of a Dynamic Urban Drainage Simulator. Water 2018, 10, 1849.

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