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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
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).
... [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) ...
Urban water systems are being stressed due to the effects of urbanization and climate change. Although household rainwater tanks are primarily used for water supply purposes, they also have the potential to provide flood benefits. However, this potential is limited for critical storms, as they become ineffective once their capacity is exceeded. This limitation can be overcome by controlling tanks as systems during rainfall events, as this can offset the timing of outflow peaks from different tanks. In this paper, the effectiveness of such systems is tested for two tank sizes under a wide range of design rainfall conditions for three Australian cities with different climates. Results show that a generic relationship exists between the ratio of tank:runoff volume and percentage peak flow reduction, irrespective of location and storm characteristics. Smart tank systems are able to reduce peak system outflows by between 35% and 85% for corresponding ranges in tank:runoff volumes of 0.15-0.8. This corresponds to a relative performance improvement on the order of 35% to 50% compared with smart tanks that are not operated in real-time. These results highlight the potential for using household rainwater tanks for mitigating urban flooding, even for extreme events.
... With PySWMM, SWMM model can be loaded and manipulated. In this way, control algorithms can be developed exclusively in Python which enables the use of functions and objects as well as storing and tracking hydraulic trends for control actions [30]. This means that control logic can be developed and implemented outside of native EPA-SWMM controls. ...
Salt Lake City, Utah has experienced several extreme urban flash flood events recently. This trend is concerning because of the historically rare extreme events that have guided design criteria for stormwater and flood control systems. There are extensive interests in maximizing the conveyance and flood control capacity of existing infrastructure and preventing damage to infrastructure and property. However, enlarging the infrastructure involves high costs and time consuming, which is unfeasible in many cases. Retrofitting the existing infrastructure with real-time control (RTC) has potential to provide an low-cost efficient solution. This paper presented the results of analysis to better understand how RTC could benefit flood control in a commercial area of Salt Lake City. The RTC strategies were tested by using the MatSWMM[ ] for modeling the drainage system and canal networks in the SH mixed use area of Salt Lake City. Results identified RTC approaches that could shave the peak flow using and delay stormwater arrival at vulnerable points; these different control strategies can be transferred to other sites and circumstances. Further investigation will focus on evaluating performance of the RTC strategies under climate change scenarios.
Keywords: real-time control, flood, drainage system, canal networks
In recent years, rain barrels (RBs) have emerged as effective stormwater management tools for mitigating urban flooding. Our previous study in Saitama City revealed that traditional RBs performed poorly in the densely populated residential areas compared to other low-impact development solutions. This study investigated the effectiveness of smart RB, comprising water pumps. These SRBs can be controlled online using smart switches that rely on local weather forecasts, to ensure that barrels are emptied before rainfall. We aimed to assess how SRBs can reduce urban pluvial flooding during continuous rainfall in Saitama City using the PySWMM model. We varied the drainage values of the RBs in simulations to control the activation of the smart switches, enabling us to evaluate the effectiveness of SRBs in flood reduction. The results showed that SRBs reduced total runoff volume by 2–10% compared to traditional RBs. Moreover, although the runoff reduction effect of the RBs was found to be independent of their size, the effectiveness of the SRBs increased proportionally with their capacity.
There have been theory-based endeavours that directly engage with AI and ML in the landscape discipline. By presenting a case that uses machine learning techniques to predict variables in a coastal environment, this paper provides empirical evidence of the forthcoming cybernetic environment, in which designers are conceptualized not as authors but as choreographers, catalyst agents, and conductors among many other intelligent agents. Drawing ideas from posthumanism, this paper argues that, to truly understand the cybernetic environment, we have to take on posthumanist ethics and overcome human exceptionalism.
Increases in the integrated application of smart sensing and control technologies enable urban drainage engineers to retrofit stormwater storage facilities with controllers actuating valves and gates to address flooding and water quality problems across the entire watershed. This objective becomes increasingly challenging because simultaneously controlling the spatially distributed storage assets to solve the stormwater issue at downstream outlet sites does not necessarily alleviate overflow at upstream storage ponds. To that end, this study presents a system-level real-time control simulation for assessing the performance of controllers in the trade-off between flooding mitigation at downstream outlets and water quality improvements at the upstream storage units like the detention pond. Using a benchmarked urban stormwater drainage model located in the southeast of Michigan, USA, with watershed-scale flooding and water quality challenges, we investigated the temporal changes in water depth, flow, and total suspended solids under different real-time control strategies, including one individual control operated only at the most downstream outlet site and two system-level controls operated at all storage detention sites. Modeling results show that the system-level control reduces peak water depth, shortens flooding duration, and removes pollutant load, by up to 7.3%, 34%, and 65%, respectively. However, system-level control does not always perform better than the individual control strategy, especially in addressing flooding duration in the most downstream outlet. This modeling work advances real-time control understanding for building adaptive stormwater management solution against water quantity and quality issues.
There have been theory-based endeavours that directly engage with AI and ML in the landscape discipline. By presenting a case that uses machine learning techniques to predict variables in a coastal environment, this paper provides empirical evidence of the forthcoming cybernetic environment , in which designers are conceptualized not as authors but as choreographers, catalyst agents, and conductors among many other intelligent agents. Drawing ideas from posthumanism, this paper argues that, to truly understand the cybernetic environment, we have to take on posthumanist ethics and overcome human exceptionalism.
The age of “smart storm water” pushes the existing stormwater infrastructure to be retrofitted with sensors, controllers, and actuators for improving system performance. Real-time control enables the development of smarter stormwater systems by controlling distributed system components like gates, pumps, and valves to adapt to changing storm events. Once the watershed is controlled, how to coordinate these dispersed controllers to achieve best global results need more theoretical support. To meet this demand, a better fundamental understanding of the coordination between local control and system-level control are needed. This paper explores the potential of using controlled smart stormwater system simulation to improve our knowledge. By presenting this modeling work, simulation outcomes of controlled stormwater systems are obtained before they become reality. Using three simulated control cases that were designed to reduce downstream peak water depth, we observed that the best at local controlled storm water sites might not be expanded to optimal system-level, watershed-scale control. Finally, a campus open-source storm water RTC simulation framework called PySWUU is compiled at the University of Utah for future hydrologic-hydraulic modeling.
Changes in climate were a growing concern during the last decade and will be even greater in the coming years. When investigating the impact from changes in the climate on urban drainage systems, two challenges are (1) what type of input rainfall data to use and (2) what parameters to use to measure the impacts. The overall objective of this study is to investigate the hydraulic performance of urban drainage systems related to changes in rainfall, and through these hydraulic parameters describe the impact of climate change. Input rainfall data represent today’s climate and three future time periods (2011–2040, 2041–2070, and 2071–2100). The hydraulic parameters used were water levels in nodes (e.g., as the number of floods, and frequency and duration of floods) and pipe flow ratio. For the study area, the number of flooded nodes and the geographical distribution of floods will increase in the future, as will both the flooding frequency and the duration of floods.
Existing stormwater systems require significant investments to meet challenges imposed by climate change, rapid urbanization, and evolving regulations. There is an unprecedented opportunity to improve urban water quality by equipping stormwater systems with low-cost sensors and controllers. This will transform their operation from static to adaptive, permitting them to be instantly "redesigned" to respond to individual storms and evolving land uses.
Most reported applications of model-predictive control (MFC) have a narrow scope, with 1-5 variables to be regulated and a comparable number of manipulated variables. Several authors have claimed that global-optimization versions of MPC (such as DMC and QDMC) should be more useful for problems in which an entire system can be operated to achieve an economic and/or technical objective. In this paper, we describe the application of MPC to a large-scale, constraint-dominated problem: the minimization of combined-sewer overflows (CSOs) in the Seattle metropolitan area. The key decision variables are flowrates at 23 locations throughout the sewer network. There are approximately 40 output variables that must be kept between lower and upper bounds. The main issues addressed in the application are: (1) definition of an appropriate objective function for on-line optimization; (2) creation and maintenance of complex system models; and (3) use of state estimation to minimize the impact of disturbances and model errors. MPC performance is compared with that of an existing heuristic (rule-based) control strategy for seven design storms, selected from historical records. A realistic, nonlinear simulation of the sewer system acts as the plant. MPC reduces CSOs by 26% (on a yearly basis) relative to the existing control strategy. This was sufficient incentive for the sewer agency to replace their heuristic control strategy with MPC.