Real-Time Operation of Pumping Systems for Urban Flood Mitigation: Single-Period vs. Multi-Period Optimization

Jafari, Fatemeh; Mousavi, S. Jamshid; Yazdi, Jafar; Kim, Joong Hoon

doi:10.1007/s11269-018-2076-4

Cited by 29 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…at study reported that the model performance was stable and the applied algorithm was robust. Based on SWMM and PSO, Jafari et al [29] divided an MPC model into two submodels, namely, the upstream and downstream sub-models.…”

Section: Introductionmentioning

confidence: 99%

“…is approach ensured the output result of the MPC system was stable and cut the computational time of the MPC system by approximately 11 minutes. Relative to the original policy, the implementation of an MPC cut the peak waterlevel violation from the target water level by 32.25% and decreased the number of pump switches by 28.5% [29]. Sadlera et al [30] applied three types of servers (personal computers, HPCs, and cloud-based computers) to run a simulated MPC system for minimizing flood risk caused by intense storms and sea level rise.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-Predicted Control System for the Real-Time Operation of an Urban Drainage System to Mitigate Urban Flood Risk: A Case Study in the Liede River Catchment, Guangzhou, China

Quan

Chen

Jiang

et al. 2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

A model-predicted control (MPC) system, which is based on a storm water management model (SWMM) and uses a multi-objective particle swarm optimization algorithm, is developed and applied to optimize the real-time operation of an urban drainage system (UDS) in the Liede River catchment, Guangzhou, China. By comparing the results of three control scenarios (i.e., the original control scenario, the current MPC, and the ideal MPC) under three typical rainfall events, the results demonstrate that the MPC system can effectively mitigate urban flood risk in engineering applications and the decision-making of the MPC system is valid. By comparing the control results of the MPC system under different rainfall return periods (e.g., 1, 2, 3, 5, and 10 years), it is found that compared with the original control scenario, the total overflow is reduced by 10%, the total overflow time is reduced by 10%, or the node overflow start time is delayed by an average of 10 minutes, and the real-time control of the MPC system is only effective when the return period of the rainfall is less than three years. It is important to explore different ways of combining the MPC system and feasible capital measures to cope with urban flood risk and challenges of climate change in future works (e.g., mean sea level rise and intense rainfall).

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Predicted Control System for the Real-Time Operation of an Urban Drainage System to Mitigate Urban Flood Risk: A Case Study in the Liede River Catchment, Guangzhou, China

Quan

Chen

Jiang

et al. 2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

show abstract

“…Algorithmic control such as using machine learning or genetic algorithms have been proposed previously for water resource management; however, both have their faults. Machine learning has primarily been utilized to predict flooding, not reduce it [14]. Similarly, genetic algorithms have issues when the scope of the area evaluated is expanded [15].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

Saliba

Bowes

Adams

et al. 2020

Water

View full text Add to dashboard Cite

Flooding in many areas is becoming more prevalent due to factors such as urbanization and climate change, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

show abstract

“…Reinforcement learning (RL) [10], a type of machine learning, is an emerging approach to stormwater system RTC that allows the creation of policies for flow control valves, pumps, and ponds within a stormwater system using simulations [11,12]. Machine learning has previously been utilized to predict flooding [13], but this strategy used perfect prediction rainfall data and was not used to reduce flooding. With all forms of RL, the ultimate goal of the agent is to learn a policy (π) for the given simulated environment.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of Flooding in Stormwater Systems Utilizing Imperfect Forecasting and Sensor Data with Deep Deterministic Policy Gradient Reinforcement Learning

Saliba¹,

Bowes²,

Adams³

et al. 2020

Preprint

View full text Add to dashboard Cite

Climate change and development have increased urban flooding, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information, are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

show abstract

Real-Time Operation of Pumping Systems for Urban Flood Mitigation: Single-Period vs. Multi-Period Optimization

Cited by 29 publications

References 20 publications

Model-Predicted Control System for the Real-Time Operation of an Urban Drainage System to Mitigate Urban Flood Risk: A Case Study in the Liede River Catchment, Guangzhou, China

Model-Predicted Control System for the Real-Time Operation of an Urban Drainage System to Mitigate Urban Flood Risk: A Case Study in the Liede River Catchment, Guangzhou, China

Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

Mitigation of Flooding in Stormwater Systems Utilizing Imperfect Forecasting and Sensor Data with Deep Deterministic Policy Gradient Reinforcement Learning

Contact Info

Product

Resources

About