Deep reinforcement learning based valve scheduling for pollution isolation in water distribution network.

Clicks: 313
ID: 65999
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #8 of 23 articles by views in mathematical biosciences and engineering : mbe

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Public water supply facilities are vulnerable to intentional intrusion. In particular, Water Distribution Network (WDN) has become one of the most important public facilities that are prone to be attacked because of its wide coverage and constant open operation. In recent years, water contamination incidents happen frequently, causing serious losses and impacts to the society. Various measures have been taken to tackle this issue. Pollution or contamination isolation by localizing the contamination via sensors and scheduling certain valves have been regarded as one of the most promising solutions. The main challenge is how to schedule water valves to effectively isolate contamination and reduce the residual concentration of contaminants in WDN. In this paper, we are motivated to propose a reinforcement learning based method for valve real time scheduling by treating the sensing data from the sensors as state, and the valve scheduling as action, thus we can learn scheduling policy from uncertain contamination events without precise characterization of contamination source. Simulation results show that our proposed algorithm can effectively isolate the contamination and reduce the risk exclosure to the customers.
Reference Key
hu2019deepmathematical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hu, Cheng Yu;Cai, Jun Yi;Zeng, De Ze;Yan, Xue Song;Gong, Wen Yin;Wang, Ling;
Journal mathematical biosciences and engineering : mbe
Year 2019
DOI
10.3934/mbe.2020006
URL
Keywords
water contamination incident reinforcement learning scheduling problem water distribution network

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.