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1. Flood Risks of Cyber‐Physical Attacks in a Smart Storm Water System

   https://doi.org/10.1029/2023WR034827  

   Lin, Chung‐Yi; Yang, Yi‐Chen_Ethan; Moazeni, Faegheh (January 2024, Water Resources Research)

   Abstract The rise in smart water technologies has introduced new cybersecurity vulnerabilities for water infrastructures. However, the implications of cyber‐physical attacks on the systems like urban drainage systems remain underexplored. This research delves into this gap, introducing a method to quantify flood risks in the face of cyber‐physical threats. We apply this approach to a smart stormwater system—a real‐time controlled network of pond‐conduit configurations, fitted with water level detectors and gate regulators. Our focus is on a specific cyber‐physical threat: false data injection (FDI). In FDI attacks, adversaries introduce deceptive data that mimics legitimate system noises, evading detection. Our risk assessment incorporates factors like sensor noises and weather prediction uncertainties. Findings reveal that FDIs can amplify flood risks by feeding the control system false data, leading to erroneous outflow directives. Notably, FDI attacks can reshape flood risk dynamics across different storm intensities, accentuating flood risks during less severe but more frequent storms. This study offers valuable insights for strategizing investments in smart stormwater systems, keeping cyber‐physical threats in perspective. Furthermore, our risk quantification method can be extended to other water system networks, such as irrigation channels and multi‐reservoir systems, aiding in cyber‐defense planning. 

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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. Shaping Streamflow Using a Real-Time Stormwater Control Network

   https://doi.org/10.3390/s18072259  

   Mullapudi, Abhiram; Bartos, Matthew; Wong, Brandon; Kerkez, Branko (July 2018, Sensors)

   “Smart” water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood. This study shows how a real-world smart stormwater system can be leveraged to shape streamflow within an urban watershed. Specifically, we coordinate releases from two internet-controlled stormwater basins to achieve desired control objectives downstream—such as maintaining the flow at a set-point, and generating interleaved waves. In the first part of the study, we describe the construction of the control network using a low-cost, open-source hardware stack and a cloud-based controller scheduling application. Next, we characterize the system’s control capabilities by determining the travel times, decay times, and magnitudes of various waves released from the upstream retention basins. With this characterization in hand, we use the system to generate two desired responses at a critical downstream junction. First, we generate a set-point hydrograph, in which flow is maintained at an approximately constant rate. Next, we generate a series of overlapping and interleaved waves using timed releases from both retention basins. We discuss how these control strategies can be used to stabilize flows, thereby mitigating streambed erosion and reducing contaminant loads into downstream waterbodies. 

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4. Data-Driven Model Predictive Control for Real-Time Stormwater Management

   Ning, Jingyun; Bowes, Benjamin; Goodall, Jonathan; Behl, Madhur (June 2022, 2022 American Control Conference)

   Low-lying coastal cities across the world are increasingly seeing flooding due to climate change and accompanying sea-level rise. Many such cities rely on old and passive stormwater infrastructure which cannot cope up with the increasing flood risk. One potential solution for addressing coastal flooding is implementing active control strategies in stormwater systems. Active stormwater control relies on rule-based strategies, which is not able to manage the increasing flood risk. Model predictive control (MPC) for stormwater flood management is getting attention over the past decade. However, building physics-based models for MPC in stormwater management is cost and time prohibitive. In this paper, we develop a data-driven approach, which utilizes unstructured state-space models for system identification and predictive control implementation. We demonstrate our results using two real stormwater network configurations, one from the Norfolk, VA region and another model of Ann Arbor region, MI, respectively. Our results indicate that MPC outperforms rule-based strategies by up to 60% of the Norfolk model and up to 90% of the Ann Arbor model in flood management. 

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5. Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks

   https://doi.org/10.3390/w11051098  

   Bowes, Benjamin D.; Sadler, Jeffrey M.; Morsy, Mohamed M.; Behl, Madhur; Goodall, Jonathan L. (May 2019, Water)

   Many coastal cities are facing frequent flooding from storm events that are made worse by sea level rise and climate change. The groundwater table level in these low relief coastal cities is an important, but often overlooked, factor in the recurrent flooding these locations face. Infiltration of stormwater and water intrusion due to tidal forcing can cause already shallow groundwater tables to quickly rise toward the land surface. This decreases available storage which increases runoff, stormwater system loads, and flooding. Groundwater table forecasts, which could help inform the modeling and management of coastal flooding, are generally unavailable. This study explores two machine learning models, Long Short-term Memory (LSTM) networks and Recurrent Neural Networks (RNN), to model and forecast groundwater table response to storm events in the flood prone coastal city of Norfolk, Virginia. To determine the effect of training data type on model accuracy, two types of datasets (i) the continuous time series and (ii) a dataset of only storm events, created from observed groundwater table, rainfall, and sea level data from 2010–2018 are used to train and test the models. Additionally, a real-time groundwater table forecasting scenario was carried out to compare the models’ abilities to predict groundwater table levels given forecast rainfall and sea level as input data. When modeling the groundwater table with observed data, LSTM networks were found to have more predictive skill than RNNs (root mean squared error (RMSE) of 0.09 m versus 0.14 m, respectively). The real-time forecast scenario showed that models trained only on storm event data outperformed models trained on the continuous time series data (RMSE of 0.07 m versus 0.66 m, respectively) and that LSTM outperformed RNN models. Because models trained with the continuous time series data had much higher RMSE values, they were not suitable for predicting the groundwater table in the real-time scenario when using forecast input data. These results demonstrate the first use of LSTM networks to create hourly forecasts of groundwater table in a coastal city and show they are well suited for creating operational forecasts in real-time. As groundwater table levels increase due to sea level rise, forecasts of groundwater table will become an increasingly valuable part of coastal flood modeling and management. 

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