<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The water loss detection and location problem has received great attention in recent years. In particular, data-driven methods have shown very promising results mainly because they can deal with uncertain data and the variability of models better than model-based methods. The main contribution of this work is an unsupervised approach to leak detection and location in water distribution networks. This approach is based on a zone division of the network, and it only requires data from a normal operation scenario of the pipe network. The proposition combines a periodic transformation and a data vector extension together with principal component analysis of leak detection. A reconstruction-based contribution index is used for determining the leak zone location. The Hanoi distribution network is employed as the case study for illustrating the feasibility of the proposal. Single leaks are emulated with varying outflow magnitudes at all nodes that represent less than 2.5% of the total demand of the network and between 3% and 25% of the node’s demand. All leaks can be detected within the time interval of a day, and the average classification rate obtained is 85.28% by using only data from three pressure sensors.</p></abstract>

The water loss detection and location problem has received great attention in recent years. In particular, data-driven methods have shown very promising results mainly because they can deal with uncertain data and the variability of models better than model-based methods. The main contribution of this work is an unsupervised approach to leak detection and location in water distribution networks. This approach is based on a zone division of the network, and it only requires data from a normal operation scenario of the pipe network. The proposition combines a periodic transformation and a data vector extension together with principal component analysis of leak detection. A reconstruction-based contribution index is used for determining the leak zone location. The Hanoi distribution network is employed as the case study for illustrating the feasibility of the proposal. Single leaks are emulated with varying outflow magnitudes at all nodes that represent less than 2.5% of the total demand of the network and between 3% and 25% of the node’s demand. All leaks can be detected within the time interval of a day, and the average classification rate obtained is 85.28% by using only data from three pressure sensors.

An Unsupervised Approach to Leak Detection and Location in Water Distribution Networks

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The water loss detection and location problem has received great attention in recent years. In particular, data-driven methods have shown very promising...

An Unsupervised Approach to Leak Detection and Location in Water Distribution Networks

Online veröffentlicht: 29. Juni 2018

Seitenbereich: 283 - 295

Eingereicht: 24. März 2017

Akzeptiert: 29. Jan. 2018

DOI: https://doi.org/10.2478/amcs-2018-0020

Schlüsselwörter
water distribution networks, leak location, unsupervised methods, principal component analysis, demand model

© 2018 Marcos Quiñones-Grueiro, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

An Unsupervised Approach to Leak Detection and Location in Water Distribution Networks

Online veröffentlicht: 29. Juni 2018

Seitenbereich: 283 - 295

Eingereicht: 24. März 2017

Akzeptiert: 29. Jan. 2018

DOI: https://doi.org/10.2478/amcs-2018-0020

Schlüsselwörterwater distribution networks, leak location, unsupervised methods, principal component analysis, demand model

© 2018 Marcos Quiñones-Grueiro, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
water distribution networks, leak location, unsupervised methods, principal component analysis, demand model