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

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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Leak detection and localization in water distribution networks by combining expert knowledge and data-driven models

Neural Computing and Applications ◽

10.1007/s00521-021-06666-4 ◽

2021 ◽

Author(s):

Adrià Soldevila ◽

Giacomo Boracchi ◽

Manuel Roveri ◽

Sebastian Tornil-Sin ◽

Vicenç Puig

Keyword(s):

Water Distribution ◽

Expert Knowledge ◽

Leak Detection ◽

Distribution Networks ◽

Water Distribution Networks ◽

Data Driven ◽

Detection And Localization

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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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Placing an ensemble of pressure sensors for leak detection in water distribution networks under measurement uncertainty

Journal of Hydroinformatics ◽

10.2166/hydro.2018.032 ◽

2018 ◽

Vol 21 (2) ◽

pp. 223-239 ◽

Cited By ~ 7

Author(s):

Ehsan Raei ◽

M. Ehsan Shafiee ◽

Mohammad Reza Nikoo ◽

Emily Berglund

Keyword(s):

Measurement Uncertainty ◽

Water Distribution ◽

Water Pressure ◽

Pressure Sensors ◽

Distribution Networks ◽

Water Distribution Networks ◽

Nsga Ii ◽

New Approach ◽

Trade Offs ◽

Selection Of

Abstract Large volumes of water are wasted through leakage in water distribution networks, and early detection of leakages is important to minimize lost water. Pressure sensors can be placed in a network to detect changes in pressure that indicate the presence of a new leak. This study presents a new approach for placing a set of pressure sensors by creating a list of candidate locations based on sensitivity to leaks that are simulated at all potential nodes in a network. The selection of a set of sensors is explored for two objectives, which are the minimization of the number of sensors and the time of detection. The non-dominated sorting genetic algorithm (NSGA-II) is used to explore trade-offs between these objectives. The effect of measurement uncertainty on the selection of sensor locations is explored by identifying alternative non-dominated fronts for different values for sensor error. The evolutionary algorithm-based approach is applied and demonstrated for the C-Town water network.

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

Water ◽

10.3390/w11071500 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1500

Author(s):

Adrià Soldevila ◽

Joaquim Blesa ◽

Rosa M. Fernandez-Canti ◽

Sebastian Tornil-Sin ◽

Vicenç Puig

Keyword(s):

Spatial Interpolation ◽

Water Distribution ◽

Historical Data ◽

Pressure Sensors ◽

Distribution Networks ◽

Data Driven ◽

Network Nodes ◽

Data Driven Approach ◽

Leak Localization ◽

Reference Pressure

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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An Integrated Bottom-Up Approach for Leak Detection in Water Distribution Networks Based on Assessing Parameters of Water Balance Model

Water ◽

10.3390/w13060867 ◽

2021 ◽

Vol 13 (6) ◽

pp. 867

Author(s):

Jie Yu ◽

Li Zhang ◽

Jinyu Chen ◽

Yao Xiao ◽

Dibo Hou ◽

...

Keyword(s):

Water Balance ◽

Water Distribution ◽

Pressure Sensors ◽

Leak Detection ◽

Distribution Networks ◽

Water Balance Model ◽

Water Distribution Networks ◽

Balance Model ◽

Bottom Up ◽

Moment Estimation

Loss of water due to leakage is a common phenomenon observed practically in all water distribution networks (WDNs). However, the leakage volume can be reduced significantly if the occurrence of leakage is detected within minimal time after its occurrence. Based on the discriminative behavior of different consumption in water balance, an integrated bottom-up water balance model is presented for leak detection in WDNs. The adaptive moment estimation (Adam) algorithm is employed to assess the parameters in the model. By analyzing the current value and the rising rate of the assessed parameters, abnormal events (e.g., leak, illegal use, or metering inaccuracy) could be detected. Furthermore, a one-step-slower strategy is proposed to estimate the weighted coefficient of pressure sensors to provide approximate location information of leak. The method was applied in a benchmark WDN and an experimental WDN to evaluate its performance. The results showed that relatively small leak could be detected in near-real-time. In addition, the method was able to identify the pressure sensors near to the leak.

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Pressure Sensor Placement for Leak Localization in Water Distribution Networks Using Information Theory

Sensors ◽

10.3390/s22020443 ◽

2022 ◽

Vol 22 (2) ◽

pp. 443

Author(s):

Ildeberto Santos-Ruiz ◽

Francisco-Ronay López-Estrada ◽

Vicenç Puig ◽

Guillermo Valencia-Palomo ◽

Héctor-Ricardo Hernández

Keyword(s):

Information Theory ◽

Pressure Sensor ◽

Water Distribution ◽

Sensor Placement ◽

Computational Cost ◽

Pressure Sensors ◽

Distribution Networks ◽

Sensor Nodes ◽

Water Distribution Networks ◽

Software Application

This paper presents a method for optimal pressure sensor placement in water distribution networks using information theory. The criterion for selecting the network nodes where to place the pressure sensors was that they provide the most useful information for locating leaks in the network. Considering that the node pressures measured by the sensors can be correlated (mutual information), a subset of sensor nodes in the network was chosen. The relevance of information was maximized, and information redundancy was minimized simultaneously. The selection of the nodes where to place the sensors was performed on datasets of pressure changes caused by multiple leak scenarios, which were synthetically generated by simulation using the EPANET software application. In order to select the optimal subset of nodes, the candidate nodes were ranked using a heuristic algorithm with quadratic computational cost, which made it time-efficient compared to other sensor placement algorithms. The sensor placement algorithm was implemented in MATLAB and tested on the Hanoi network. It was verified by exhaustive analysis that the selected nodes were the best combination to place the sensors and detect leaks.

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An iterative method for leakage zone identification in water distribution networks based on machine learning

Structural Health Monitoring ◽

10.1177/1475921720950470 ◽

2020 ◽

pp. 147592172095047

Author(s):

Jingyu Chen ◽

Xin Feng ◽

Shiyun Xiao

Keyword(s):

Random Forest ◽

Iterative Method ◽

Water Distribution ◽

Pressure Sensors ◽

Distribution Networks ◽

Random Forest Classifier ◽

Identification Accuracy ◽

Water Distribution Networks ◽

Minimum Number ◽

Leakage Characteristics

For leakage identification in water distribution networks, if each node is used as a category label of the classifier model, the accuracy of the classifier model will be low because of similar leakage characteristics. By clustering the nodes with similar leakage characteristics and using all the possible combinations of leakages as the category labels of the classifier model, the accuracy of the classifier model for leakage location can be improved. An iterative method combining k-means clustering with the random forest classifier is proposed to identify the leakage zones. In each iteration, k-means clustering is used to divide the leakage zone identified in the previous iterations into two zones, and then, the random forest classifier is used to identify the leakage zones and the number of leakages in each leakage zone. As the number of iterations increases, the number of candidate leakage zones and sensors that conduct leakage zone identification decreases. Thus, feature selection can be used in each iteration to select the minimum number of sensors for model training without affecting identification accuracy. Three leakage scenarios are considered: a single leakage, two simultaneous leakages, and four simultaneous leakages. A benchmark case is presented in this study to demonstrate the effectiveness of the proposed method. The influences of the number of pressure sensors and Gaussian noise level on the identification results are also discussed. Results indicate that the proposed method is effective for identifying simultaneous leakages.

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

International Journal of Applied Mathematics and Computer Science ◽

10.2478/amcs-2018-0020 ◽

2018 ◽

Vol 28 (2) ◽

pp. 283-295 ◽

Cited By ~ 3

Author(s):

Marcos Quiñones-Grueiro ◽

Cristina Verde ◽

Alberto Prieto-Moreno ◽

Orestes Llanes-Santiago

Keyword(s):

Water Distribution ◽

Pressure Sensors ◽

Leak Detection ◽

Distribution Networks ◽

Water Distribution Networks ◽

Normal Operation ◽

Time Interval ◽

Classification Rate ◽

Unsupervised Approach ◽

Leak Detection And Location

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.

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