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1. Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning

   https://doi.org/10.2166/hydro.2020.080  

   Bowes, Benjamin D.; Tavakoli, Arash; Wang, Cheng; Heydarian, Arsalan; Behl, Madhur; Beling, Peter A.; Goodall, Jonathan L. (October 2020, Journal of Hydroinformatics)

   null (Ed.)

   Abstract Flooding in coastal cities is increasing due to climate change and sea-level rise, stressing the traditional stormwater systems these communities rely on. Automated real-time control (RTC) of these systems can improve performance, and creating control policies for smart stormwater systems is an active area of study. This research explores reinforcement learning (RL) to create control policies to mitigate flood risk. RL is trained using a model of hypothetical urban catchments with a tidal boundary and two retention ponds with controllable valves. RL's performance is compared to the passive system, a model predictive control (MPC) strategy, and a rule-based control strategy (RBC). RL learns to proactively manage pond levels using current and forecast conditions and reduced flooding by 32% over the passive system. Compared to the MPC approach using a physics-based model and genetic algorithm, RL achieved nearly the same flood reduction, just 3% less than MPC, with a significant 88× speedup in runtime. Compared to RBC, RL was able to quickly learn similar control strategies and reduced flooding by an additional 19%. This research demonstrates that RL can effectively control a simple system and offers a computationally efficient method that could scale to RTC of more complex stormwater systems. 

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2. Reinforcement learning-based real-time control of coastal urban stormwater systems to mitigate flooding and improve water quality

   https://doi.org/10.1039/D1EW00582K  

   Bowes, Benjamin D.; Wang, Cheng; Ercan, Mehmet B.; Culver, Teresa B.; Beling, Peter A.; Goodall, Jonathan L. (March 2022, Environmental Science: Water Research & Technology)

   Real-time control of stormwater systems can reduce flooding and improve water quality. Current industry real-time control strategies use simple rules based on water quantity parameters at a local scale. However, system-level control methods that also incorporate observations of water quality could provide improved control and performance. Therefore, the objective of this research is to evaluate the impact of local and system-level control approaches on flooding and sediment-related water quality in a stormwater system within the flood-prone coastal city of Norfolk, Virginia, USA. Deep reinforcement learning (RL), an emerging machine learning technique, is used to learn system-level control policies that attempt to balance flood mitigation and treatment of sediment. RL is compared to the conventional stormwater system and two methods of local-scale rule-based control: (i) industry standard predictive rule-based control with a fixed detention time and (ii) rules based on water quality observations. For the studied system, both methods of rule-based control improved water quality compared to the passive system, but increased total system flooding due to uncoordinated releases of stormwater. An RL agent learned controls that maintained target pond levels while reducing total system flooding by 4% compared to the passive system. When pre-trained from the RL agent that learned to reduce flooding, another RL agent was able to learn to decrease TSS export by an average of 52% compared to the passive system and with an average of 5% less flooding than the rule-based control methods. As the complexity of stormwater RTC implementations grows and climate change continues, system-level control approaches such as the RL used here will be needed to help mitigate flooding and protect water quality. 

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3. Compounding Effects of Salinity and Compaction on Hydraulic Properties of Roadside Stormwater Control Measures

   https://doi.org/10.5194/egusphere-egu23-8262  

   Archibald, L. (April 2023, EGU General Assembly 2023, Vienna, Austria,)

   Stormwater control measures (SCMs) such as retention basins, bioswales, and bioinfiltration systems are used to reduce peak flows and remove pollutants from stormwater in temperate urban landscapes. However, the application of de-icing salts to roadways can substantially increase the salinity of stormwater basin media (i.e., engineered soil), likely impacting the physical properties of these soils. Further, SCM soils can become moderately compacted, potentially altering the extent and effects of salinization on soil physical properties. Although many studies have documented the high salinity of roadside soils in winter, the effects of salinity on soil hydraulic properties is not well understood, especially in the context of urban stormwater basins. Here, we compared the water retention properties (spanning pressure potentials of -10 to -1,000,000 hPa) of salinity-affected stormwater media (1-100 dS m-1, using Na+ and Mg2+ salts) that was either uncompacted or compacted. The effects of salinity on both matric and osmotic potential included shifts in the plant-available range with the magnitude depending on a combination of salt type and concentration. We attribute these changes to salinity inducing shifts in both surface tension and pore size distributions. Further, compaction increased the severity of salinization under low salinity conditions but not high. Climate change may increase the number and intensity of snow events in many temperate urban regions, which may increase salt loads to stormwater control measures, exacerbating the aforementioned effects. 

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4. Open storm: a complete framework for sensing and control of urban watersheds

   https://doi.org/10.1039/C7EW00374A  

   Bartos, Matthew; Wong, Brandon; Kerkez, Branko (January 2018, Environmental Science: Water Research & Technology)

   Leveraging recent advances in technologies surrounding the Internet of Things , “smart” water systems are poised to transform water resources management by enabling ubiquitous real-time sensing and control. Recent applications have demonstrated the potential to improve flood forecasting, enhance rainwater harvesting, and prevent combined sewer overflows. However, adoption of smart water systems has been hindered by a limited number of proven case studies, along with a lack of guidance on how smart water systems should be built. To this end, we review existing solutions, and introduce open storm —an open-source, end-to-end platform for real-time monitoring and control of watersheds. Open storm includes (i) a robust hardware stack for distributed sensing and control in harsh environments (ii) a cloud services platform that enables system-level supervision and coordination of water assets, and (iii) a comprehensive, web-based “how-to” guide, available on open-storm.org, that empowers newcomers to develop and deploy their own smart water networks. We illustrate the capabilities of the open storm platform through two ongoing deployments: (i) a high-resolution flash-flood monitoring network that detects and communicates flood hazards at the level of individual roadways and (ii) a real-time stormwater control network that actively modulates discharges from stormwater facilities to improve water quality and reduce stream erosion. Through these case studies, we demonstrate the real-world potential for smart water systems to enable sustainable management of water resources. 

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5. Reachability Analysis as a Design Tool for Stormwater Systems

   https://doi.org/10.1109/sustech.2018.8671362  

   Chapman, Margaret P.; Smith, Kevin M.; Cheng, Victoria; Freyberg, David L.; Tomlin, Claire J. (November 2018, 2018 IEEE Conference on Technologies for Sustainability (SusTech))

   Effective stormwater management requires systems that operate safely and deliver improved environmental outcomes in a cost-effective manner. However, current design practices typically evaluate performance assuming that a given system starts empty and operates independently from nearby stormwater infrastructure. There is a conspicuous need for more realistic design-phase indicators of performance that consider a larger set of initial conditions and the effects of coupled dynamics. To this end, we apply a control-theoretic method, called reachability analysis, to produce a more objective measure of system robustness. We seek two primary contributions in this work. First, we demonstrate how the application of reachability analysis to a dynamic model of a stormwater network can characterize the set of initial conditions from which every element in the network can avoid overflowing under a given surface runoff signal of finite duration. This is, to the authors’ knowledge, the first published application of reachability analysis to stormwater systems. Our second contribution is to offer an interpretation of the outcomes of the proposed reachability analysis as a measure of system robustness that can provide useful information when making critical design decisions. We illustrate the effectiveness of this method in revealing the trade-offs of particular design choices relative to a desired level of robustness. 

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