Convolutional Neural Network for Burst Detection in Smart Water Distribution Systems-Reference-Cited by-同舟云学术

Convolutional Neural Network for Burst Detection in Smart Water Distribution Systems

Published:2023-05-09 Issue:9 Volume:37 Page:3729-3743
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Jun Sanghoon^ORCID,Lansey Kevin E.

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11269-023-03524-x.pdf

Reference28 articles.

1. American Water Works Association (AWWA) (2008) Water Audits and Loss Control Programs: M36, vol 36. American Water Works Association, Washington, DC

2. Blokker EJM (2010) Stochastic water demand modelling for a better understanding of hydraulics in water distribution networks. Ph.D. thesis, Dept of Water Manag Delft Univ of Technol

3. Duan L, Xie M, Wang J, Bai T (2018) Deep learning enabled intelligent fault diagnosis: Overview and applications. J Intell Fuzzy Syst 35(5):5771–5784

4. Fang Q, Zhang J, Xie C, Yang Y (2019) Detection of multiple leakage points in water distribution networks based on convolutional neural networks. Water Supply 19(8):2231–2239

5. Gupta A, Kulat KD (2018) A selective literature review on leak management techniques for water distribution system. Water Resour Manag 32:3247–3269

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