Centrality-Guided Node Selection for Efficient Water Leak Detection

Water Distribution Networks (WDNs) are critical infrastructures that require continuous monitoring to ensure efficient operation and mitigate water loss caused by leaks. The strategic placement of sensors within these networks is essential for timely leak detection and localization. Selecting the most effective nodes for sensor placement, however, remains a challenging problem. Centrality measures, widely used in network science to quantify the node importance, offer a promising approach to guide the sensor deployment. This study investigates the impact of various centrality measures on node selection for leak detection in WDNs. We address the leak-detection problem using a two-stage methodology. First, we derive compact and informative feature representations from pressure matrices using the encoder of an autoencoder. Then, we apply a binary classifier to identify anomalous embeddings. We apply our method to two topologically different WDNs, exploring different centrality measures for sparse sensor deployment. Results show that in looped, high-redundancy topologies, a reduced set of strategically placed sensors maintain or even improve detection accuracy. In contrast, more fragile tree-like networks experience performance loss when sensors are reduced. To explain this structural behavior, we introduce a metric that quantifies a network's structural vulnerability. Taken together, our findings offer practical guidance for water utilities seeking to balance leak-detection speed, accuracy, and cost through topology-based sensor placement.