Supervised Machine Learning Approaches for Leak Localization in Water Distribution Systems: Impact of Complexities of Leak Characteristics-Reference-Cited by-同舟云学术

Supervised Machine Learning Approaches for Leak Localization in Water Distribution Systems: Impact of Complexities of Leak Characteristics

Published:2023-08 Issue:8 Volume:149 Page:
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Basnet Lochan¹^ORCID,Brill Downey²,Ranjithan Ranji²,Mahinthakumar Kumar³^ORCID

Affiliation:

1. Ph.D. Student, Dept. of Civil Engineering, North Carolina State Univ., Raleigh, NC 27606 (corresponding author). ORCID: .

2. Professor, Dept. of Civil Engineering, North Carolina State Univ., Raleigh, NC 27606.

3. Professor, Dept. of Civil Engineering, North Carolina State Univ., Raleigh, NC 27606. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JWRMD5.WRENG-6047

Reference39 articles.

1. Abadi M. et al. 2016. “Tensor flow: Large-scale machine learning on heterogeneous distributed systems.” Accessed December 13 2022. https://arxiv.org/abs/1603.04467.

2. Probabilistic multiple model neural network based leak detection system: Experimental study

3. Successive Linear Approximation Methods for Leak Detection in Water Distribution Systems

4. Modelling uncertainty for leak localization in Water Networks

5. Leak Detection and Topology Identification in Pipelines Using Fluid Transients and Artificial Neural Networks

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A two-phase approach for leak detection and localization in water distribution systems using wavelet decomposition and machine learning;Computers & Industrial Engineering;2024-11

2. The performance of encoder–decoder neural networks for leak detection in water distribution networks;Water Supply;2024-07-26

3. Assessing energy efficiency of water services and its drivers: A case study from water companies in England and Wales;Journal of Water Process Engineering;2024-07

4. A Unified Spatial-Pressure Sensitivity Partitioning and Leakage Detection Method within a Deep Learning Framework;Water;2024-02-09

5. Wasserstein-Enabled Leaks Localization in Water Distribution Networks;Water;2024-01-27