Spatial Cluster Analysis of Bursting Pipes in Water Supply Networks

Abstract

Urban water supply network is a hidden underground asset; therefore, it is difficult to make a direct diagnosis towards the faults of pipe network by means of the detecting technology. Based on the historical data of the rupture of water supply pipe network, a spatial cluster analysis will be carried out towards the historical data of spatial fault in water supply pipeline, using density- based cluster analysis and the DBSCAN algorithm. K-neighborhood graph is well-used in optimizing the input parameter radius of neighborhood (Eps) and density (MinPts) and classifying the historical data of the rupture of pipeline. According to the pipe network failure point coordinates (X,Y), adopting density-based cluster analysis, a danger level classification will be researched without knowing the reasons of the rupture of pipe network, which lays the foundation for finding out pipeline rupture in local areas in the future. Meanwhile, a noise analysis will be processed in terms of the historical data of pipeline rupture in water supply network, avoiding the ineffective data caused by recording errors and regular analysis. This research focuses on the reasons that result in the rupture of water supply network, which plays a significant role in the identification of high-density and high-danger zones in water supply networks.