Leveraging open source software and parallel computing for model predictive control of urban drainage systems using EPA-SWMM5. (October 2019)

• Record Type: Journal Article
• Authors: Sadler, Jeffrey M.; Goodall, Jonathan L.; Behl, Madhur; Morsy, Mohamed M.; Culver, Teresa B.; Bowes, Benjamin D.
• Is Part Of: Environmental modelling & software. Volume 120(2019)
• Publication Date: 2019-10
• Subjects: Urban flooding -- Real-time control -- Parallel processing -- Model predictive control -- Stormwater -- Urban drainage modeling; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.07.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Active stormwater control will play an increasingly important role in mitigating urban flooding, which is becoming more common with climate change and sea level rise. In this paper we describe and demonstrate swmm_mpc, software developed for simulating model predictive control (MPC) for urban drainage systems using open source software (Python and the EPA Stormwater Management Model version 5 (SWMM5)). Swmm_mpc uses an evolutionary algorithm as an optimizer and supports parallel processing. In the demonstration case for a hypothetical, tidally-influenced urban drainage system, the swmm_mpc control policies for two storage units achieved its objectives of 1) practically eliminating flooding and 2) maintaining the water level at the storage units close to a target level. Although the current swmm_mpc workflow was feasible for a simple model using a desktop PC, a high-performance computer or cloud-based computer with more computational cores would likely be needed for most real-world models. Highlights: Open-source implementation of model predictive control for EPA-SWMM5, swmm_mpc. Evolutionary algorithm used to select effective control policy at each time step. Parallel processing of genetic algorithm significantly reduces run-time. Control policy from swmm_mpc minimizes flooding and maintains target water level. Computational cost measured for personal, high-performance, and cloud-based computers.