From Theory to Practice: Robust and Automated Data-Driven Leakage Detection in Water Distribution Networks

Reliable leak detection in water distribution networks is essential for reducing water losses, costs, and operational risks. Here, we present an improved semisupervised pressure-based algorithm that enables near-real-time leak detection through two key features: (1) automation of sensor selection and retraining times, avoiding manual tuning and prior knowledge of leak conditions, and (2) an adaptive, nonparametric cumulative sum (CUSUM) with a self-starting scheme, robust to noisy and non-normal data. Tested on a real-world network data set with synthetic nonoverlapping leaks, the algorithm detects all leakages without false alarms, demonstrating strong potential for practical deployment.