Clustering-Learning Approach to the Localization of Leaks in Water Distribution Networks-Reference-Cited by-同舟云学术

Clustering-Learning Approach to the Localization of Leaks in Water Distribution Networks

Published:2022-04 Issue:4 Volume:148 Page:
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Romero Luis¹^ORCID,Blesa Joaquim²^ORCID,Puig Vicenç³,Cembrano Gabriela⁴^ORCID

Affiliation:

1. Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Carrer Llorens Artigas, 4-6, 08028 Barcelona, Spain (corresponding author). ORCID: .

2. Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Carrer Llorens Artigas, 4-6, 08028 Barcelona, Spain; Supervision, Safety and Automatic Control Research Center (CS2AC) of the Universitat Politécnica de Catalunya, Campus de Terrassa, Gaia Bldg., Rambla Sant Nebridi, 22, 08222 Terrassa, Barcelona, Spain. ORCID: .

3. Professor, Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Carrer Llorens Artigas, 4-6, 08028 Barcelona, Spain; Supervision, Safety and Automatic Control Research Center (CS2AC) of the Universitat Politécnica de Catalunya, Campus de Terrassa, Gaia Bldg., Rambla Sant Nebridi, 22, 08222 Terrassa, Barcelona, Spain.

4. Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Carrer Llorens Artigas, 4-6, 08028 Barcelona, Spain. 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/%28ASCE%29WR.1943-5452.0001527

Reference33 articles.

1. Agarap A. 2018. “Deep learning using rectified linear units (relu).” Preprint submitted March 22 2018. http://arxiv.org/1803.08375.

2. A novel data-driven leak detection and localization algorithm using the Kantorovich distance

3. Bishop, C. 2006. “Pattern recognition and machine learning.” In Probability distributions—The exponential family. New York: Springer.

4. Modelling uncertainty for leak localization in Water Networks

5. Model-based leak detection and location in water distribution networks considering an extended-horizon analysis of pressure sensitivities

Cited by 13 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. The performance of encoder–decoder neural networks for leak detection in water distribution networks;Water Supply;2024-07-26

2. Leak localization in an urban water distribution network using a LSTM deep neural network;IFAC-PapersOnLine;2024

3. Burst detection based on multi-time monitoring data from multiple pressure sensors in district metering areas;Water Supply;2023-08-23

4. Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks;Journal of Imaging;2023-03-14

5. A new method of diagnostic row reasoning based on trivalent residuals;Expert Systems with Applications;2023-03