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Author Cheng Wang; Benjamin Bowes; Arash Tavakoli; Stephen Adams; Jonathan Goodall; Peter Beling pdf  isbn
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  Title Smart Stormwater Control Systems: A Reinforcement Learning Approach Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 2-13  
  Keywords Reinforcement Learning, Stormwater, Flooding Control.  
  Abstract Flooding poses a significant and growing risk for many urban areas. Stormwater systems are typically used to control flooding, but are traditionally passive (i.e. have no controllable components). However, if stormwater systems are retrofitted with valves and pumps, policies for controlling them in real-time could be implemented to enhance system performance over a wider range of conditions than originally designed for. In this paper, we propose an autonomous, reinforcement learning (RL) based, stormwater control system that aims to minimize flooding during storms. With this approach, an optimal control policy can be learned by letting an RL agent interact with the system in response to received reward signals. In comparison with a set of static control rules, RL shows superior performance on a wide range of artificial storm events. This demonstrates RL's ability to learn control actions based on observation and interaction, a key benefit for dynamic and ever-changing urban areas.  
  Address Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor (up) Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-1 ISBN 2411-3387 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes cw8xk@virginia.edu Approved no  
  Call Number Serial 2202  
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