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Leveraging Open Source Software and Parallel Computing for Model Predictive Control Simulation of Urban Drainage Systems Using EPA-SWMM5 and Python

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Abstract

The active control of stormwater systems is a potential solution to increased street flooding in low-lying, low-relief coastal cities due to climate change and accompanying sea level rise. Model predictive control (MPC) has been shown to be a successful control strategy generally and as well as for managing urban drainage specifically. This research describes and demonstrates the implementation of MPC for urban drainage systems using open source software (Python and The United States Environmental Protection Agency (EPA) Storm Water Management Model (SWMM5). The system was demonstrated using a simplified use case in which an actively-controlled outlet of a detention pond is simulated. The control of the pond’s outlet influences the flood risk of a downstream node. For each step in the SWMM5 model, a series of policies for controlling the outlet are evaluated. The best policy is then selected using an evolutionary algorithm. The policies are evaluated against an objective function that penalizes primarily flooding and secondarily deviation of the detention pond level from a target level. Freely available Python libraries provide the key functionality for the MPC workflow: step-by-step running of the SWMM5 simulation, evolutionary algorithm implementation, and leveraging parallel computing. For perspective, the MPC results were compared to results from a rule-based approach and a scenario with no active control. The MPC approach produced a control policy that largely eliminated flooding (unlike the scenario with no active control) and maintained the detention pond’s water level closer to a target level (unlike the rule-based approach).

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... [32]) and PySWMM (Python Wrapper for Stormwater Management Model, developed by EmNet LLC in South Bend, the United States [33]). DEAP (v1.3.0) is an evolutionary computation framework developed for solving real-world problems by applying evolutionary algorithms to simulation modules and is used to select the decision variable values i.e., the degree and timing of the opening of the tank outlets (see Sections 2.2 and 2.3) [34]. The NSGA-II genetic algorithm (Non dominated sorting genetic algorithm) was chosen in the DEAP package, as its variants have already been used successfully for the optimization of urban stormwater systems [29,35,36]. ...
... The stormwater simulation model used to evaluate the peak flow performance of the controlled tank systems was SWMM (Stormwater Management Model, developed by the United States Environmental Protection Agency [34]). SWMM (v5.1.012) ...
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