Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation
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In this article, the authors proposed a data-driven method for leak localization in water distribution networks, which 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.Abstract:
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.read more
Citations
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Journal ArticleDOI
Review of model-based and data-driven approaches for leak detection and location in water distribution systems
TL;DR: It is seen that model-based approaches require highly calibrated hydraulic models, and their accuracies are sensitive to modeling and measurement uncertainties, and neither of these approaches can handle anomalous variations caused by unexpected water demands.
Journal ArticleDOI
Leak Localization Method for Water-Distribution Networks Using a Data-Driven Model and Dempster–Shafer Reasoning
Adrià Soldevila,Joaquim Blesa,Tom Nørgaard Jensen,Sebastian Tornil-Sin,Rosa M. Fernandez-Canti,Vicenç Puig +5 more
TL;DR: A new data-driven method for leak localization in water-distribution networks (WDNs) using the information provided by a set of pressure sensors installed in some internal network nodes in addition to flow and pressure measurements from inlet nodes is presented.
Journal ArticleDOI
Assessing the Potential of LPWAN Communication Technologies for Near Real-Time Leak Detection in Water Distribution Systems.
TL;DR: In this article, the authors compared the potential of low-power wide-area network (LPWAN) technologies for near real-time leak detection in water distribution systems (WDS).
Journal ArticleDOI
Literature Review of Data Analytics for Leak Detection in Water Distribution Networks: A Focus on Pressure and Flow Smart Sensors
TL;DR: In this paper , a literature review is presented to develop a step-by-step analytic framework for the leakage detection process based on flow and pressure data collected from water distribution networks and the main steps of the data analytic for leakage detection are: setting up the goals, data collection, preparing the gathered data, analyzing the prepared data, and method evaluation.
Journal ArticleDOI
Data-Driven Leak Localization in Urban Water Distribution Networks Using Big Data for Random Forest Classifier
TL;DR: A Random Forest classifier is used to detect leak locations on two different sized water distribution networks with sparse sensor placement and results indicate that the prediction model provides the greatest accuracy for the largest leaks.
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