Clustering-Based Partitioning of Water Distribution Networks for Leak Zone Location

In recent years, there has been an increase in leak zone identification strategies in water distribution networks. This paper presents an analysis of the effect network partitioning techniques have on the performance of leak zone location methodologies. An SVM classifier is used to identify the leak zone location. The effect of the following clustering methods for network partitioning is analyzed: k-medoids, agglomerative clustering, DBSCAN, and Girvan-Newman algorithm. Both topological and hydraulic variables are considered when performing the clustering with three different sensor configurations. The results obtained demonstrate that the effect of each clustering method on the leak location performance is similar for both types of variables.