Addressing Practical Challenges of Stochastic Process Control for Leakage Detection in Water Distribution Networks: A Comparative Analysis

Various leakage detection algorithms have been proposed in the literature to pursue prompt leakage identification in water distribution networks using sensor data. Some use stochastic process control (SPC) methods, where the leak identification is considered an anomaly detection problem. However, several practical challenges emerge from SPC in leakage detection, including outliers, random fluctuations, and non-normally distributed and autocorrelated data. Here, we contribute a comparative analysis of seven advanced SPC techniques based on cumulative sum for leakage identification in water distribution networks, selected based on an extensive literature review of approximately 100 publications. We test their usability for leakage detection by integrating them into a state-of-the-art data-driven detection method and demonstrating them on the L-Town benchmark network. Ultimately, this study offers actionable recommendations to guide the selection of change point detection methods for leakage detection: our results indicate that transformations are best combined with robust statistics. Nonparametric and adaptive methods are suited to retrieve reliable alarms under heterogeneous conditions. Lastly, incorporating a decorrelation step improves the detection performance if irregular water demand patterns occur in the network.