Clustering-Learning Approach to the Localization of Leaks in Water Distribution Networks
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 4
Abstract
Leak detection and localization in water distribution networks (WDNs) is of great significance for water utilities. This paper proposes a leak localization method that requires hydraulic measurements and structural information of the network. It is composed by an image encoding procedure and a recursive clustering/learning approach. Image encoding is carried out using Gramian angular field (GAF) on pressure measurements to obtain images for the learning phase (for all possible leak scenarios). The recursive clustering/learning approach divides the considered region of the network into two sets of nodes using graph agglomerative clustering (GAC) and trains a deep neural network (DNN) to discern the location of each leak between the two possible clusters, using each one of them as inputs to future iterations of the process. The achieved set of DNNs is hierarchically organized to generate a classification tree. Actual measurements from a leak event occurred in a real network are used to assess the approach, comparing its performance with another state-of-the-art technique, and demonstrating the capability of the method to regulate the area of localization depending on the depth of the route through the tree.
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Data Availability Statement
All data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. Specifically, there is a hard restriction in the sharing of information about the studied network: features, location, the hydraulic information from the real leak event. The associated codes could be shared upon reasonable request.
Acknowledgments
The authors want to thank the Spanish national project “DEOCS (DPI2016-76493-C3-3-R)” project (which is finished nowadays) by its continuation: “L-BEST Project (PID2020-115905RB-C21) funded by MCIN/ AEI /10.13039/501100011033” and the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Joaquim Blesa acknowledges the support from the Serra Húnter program.
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History
Received: Nov 24, 2020
Accepted: Nov 16, 2021
Published online: Jan 27, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 27, 2022
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