Data-Driven Approach for Leak Localization in Water Distribution Networks Using Pressure Sensors and Spatial Interpolation

This paper presents a new data-driven method for leak localization in water distribution networks. The proposed method relies on the use of available pressure measurements in some selected internal network nodes and on the estimation of the pressure at the remaining nodes using Kriging spatial interpolation. Online leak localization is attained by comparing current pressure values with their reference values. Supported by Kriging; this comparison can be performed for all the network nodes, not only for those equipped with pressure sensors. On the one hand, reference pressure values in all nodes are obtained by applying Kriging to measurement data previously recorded under network operation without leaks. On the other hand, current pressure values at all nodes are obtained by applying Kriging to the current measured pressure values. The node that presents the maximum difference (residual) between current and reference pressure values is proposed as a leaky node candidate. Thereafter, a time horizon computation based on Bayesian reasoning is applied to consider the residual time evolution, resulting in an improved leak localization accuracy. As a data-driven approach, the proposed method does not need a hydraulic model; only historical data from normal operation is required. This is an advantage with respect to most data-driven methods that need historical data for the considered leak scenarios. Since, in practice, the obtained leak localization results will strongly depend on the number of available pressure measurements and their location, an optimal sensor placement procedure is also proposed in the paper. Three different case studies illustrate the performance of the proposed methodologies.

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Leak Localization in Water Distribution Networks Using Pressure and Data-Driven Classifier Approach

Water ◽

10.3390/w12010054 ◽

2019 ◽

Vol 12 (1) ◽

pp. 54 ◽

Cited By ~ 4

Author(s):

Congcong Sun ◽

Benjamí Parellada ◽

Vicenç Puig ◽

Gabriela Cembrano

Keyword(s):

Water Distribution ◽

Distribution Networks ◽

Water Distribution Networks ◽

Data Driven ◽

Time Step ◽

Water Losses ◽

Localization Accuracy ◽

Linear Discriminant ◽

Data Driven Approach ◽

Leak Localization

Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses through leak detection and localization, timely and efficiently using advanced techniques is an urgent humanitarian need. There are numerous methods being used to localize water leaks in WDNs through constructing hydraulic models or analyzing flow/pressure deviations between the observed data and the estimated values. However, from the application perspective, it is very practical to implement an approach which does not rely too much on measurements and complex models with reasonable computation demand. Under this context, this paper presents a novel method for leak localization which uses a data-driven approach based on limit pressure measurements in WDNs with two stages included: (1) Two different machine learning classifiers based on linear discriminant analysis (LDA) and neural networks (NNET) are developed to determine the probabilities of each node having a leak inside a WDN; (2) Bayesian temporal reasoning is applied afterwards to rescale the probabilities of each possible leak location at each time step after a leak is detected, with the aim of improving the localization accuracy. As an initial illustration, the hypothetical benchmark Hanoi district metered area (DMA) is used as the case study to test the performance of the proposed approach. Using the fitting accuracy and average topological distance (ATD) as performance indicators, the preliminary results reaches more than 80% accuracy in the best cases.

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Leak localization in water distribution networks using a mixed model-based/data-driven approach

Control Engineering Practice ◽

10.1016/j.conengprac.2016.07.006 ◽

2016 ◽

Vol 55 ◽

pp. 162-173 ◽

Cited By ~ 25

Author(s):

Adrià Soldevila ◽

Joaquim Blesa ◽

Sebastian Tornil-Sin ◽

Eric Duviella ◽

Rosa M. Fernandez-Canti ◽

...

Keyword(s):

Water Distribution ◽

Mixed Model ◽

Distribution Networks ◽

Water Distribution Networks ◽

Data Driven ◽

Model Based ◽

Data Driven Approach ◽

Leak Localization

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A Fully Data-Driven Approach for Leak Localization in Water Distribution Networks

10.23919/ecc54610.2021.9654928 ◽

2021 ◽

Author(s):

Luis Romero ◽

Vicenc Puig ◽

Gabriela Cembrano ◽

Joaquim Blesa ◽

Jordi Meseguer

Keyword(s):

Water Distribution ◽

Distribution Networks ◽

Water Distribution Networks ◽

Data Driven ◽

Data Driven Approach ◽

Leak Localization

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Robust Data-Driven Leak Localization in Water Distribution Networks Using Pressure Measurements and Topological Information

Sensors ◽

10.3390/s21227551 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7551

Author(s):

Débora Alves ◽

Joaquim Blesa ◽

Eric Duviella ◽

Lala Rajaoarisoa

Keyword(s):

Water Distribution ◽

Pressure Sensors ◽

Distribution Networks ◽

Water Distribution Networks ◽

Data Driven ◽

Pressure Measurements ◽

Leak Pressure ◽

Flow Measurements ◽

Topological Information ◽

Probable Path

This article presents a new data-driven method for locating leaks in water distribution networks (WDNs). It is triggered after a leak has been detected in the WDN. The proposed approach is based on the use of inlet pressure and flow measurements, other pressure measurements available at some selected inner nodes of the WDN, and the topological information of the network. A reduced-order model structure is used to calculate non-leak pressure estimations at sensed inner nodes. Residuals are generated using the comparison between these estimations and leak pressure measurements. In a leak scenario, it is possible to determine the relative incidence of a leak in a node by using the network topology and what it means to correlate the probable leaking nodes with the available residual information. Topological information and residual information can be integrated into a likelihood index used to determine the most probable leak node in the WDN at a given instant k or, through applying the Bayes’ rule, in a time horizon. The likelihood index is based on a new incidence factor that considers the most probable path of water from reservoirs to pressure sensors and potential leak nodes. In addition, a pressure sensor validation method based on pressure residuals that allows the detection of sensor faults is proposed.

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