Spatiotemporal Scan Statistics for the Identification of Density-Based Clusters of Pipe Failure Events in Drinking Water Distribution Systems

摘要

In this study, a hierarchical density-based spatial clustering of applications with noise algorithm (HDBSCAN) is implemented for the identification of clusters of pipe failures in drinking water distribution systems. The clustering method is adapted to incorporate the space-time distances between failure events to capture spatiotemporal clusters in the failure data. The proposed approach is demonstrated on a dataset of pipe failures retrieved from the maintenance records of a real-life, large-scale water utility. The clustering approach was able to capture clusters of varying spatial and temporal spread. Overall, this study targets identifying patterns of pipe breaks, which can improve the understanding of the significant factors that promote the deterioration of the pipe infrastructure.