Robust Water Leak Detection and Localization with Graph Signal Processing

Water is a resource that has to be managed properly. Nevertheless, a sizable amount of water is lost each year because of leaks in Water Distribution Networks (WDNs). The need for trustworthy and efficient leak detection and localization systems is therefore an urgent necessity. For this reason, different solutions have been put out in recent years. Due to their outstanding performance, data-driven methods are among those that are gaining the most popularity. However, the performance of data-driven approaches depend on the coherence between data on which they are trained and data on which they are tested. For example, if the acquired test data look corrupted and incoherent with training ones due to sensor failure, the performance of the overall system may be severely hindered. In this work we present a resilient water leak detection and localization algorithm. It is based on two main steps: the first step analyzes acquired data to possibly recover corrupted ones by means of graph interpolation; the second step finds leaks exploiting an autoencoder-based anomaly detector proposed in the literature. The results show that the suggested approach for signal recovery by means of graph interpolation enables the detector to work in situations in which it would otherwise fail. In doing so, we address a problem that has so far received little attention in the literature: potential sensor failures when acquiring data.