Abstract
This article presents a new data-driven method for leak localization in water-distribution networks (WDNs). The method uses 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. Pressure measurements are recorded under leak-free network operation, and a WDN data-driven model of the pressure at each sensed node is adjusted. The pressure estimation from this model is complemented by a Kriging spatial interpolation technique to estimate the pressure in the nodes that are not sensed, leading to a pressure reference map. Leak localization is based on the comparison of this reference pressure map with the current pressure map that is obtained by applying Kriging directly to the pressure measurements provided by sensors. The key element in this comparison is the use of the Dempster–Shafer theory for reasoning under uncertainty. The successful application of the proposed methodology to two real-data case studies is presented.
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