Abstract
Water, a critical resource, suffers from significant losses due to leakages in Water Distribution Networks (WDN). These losses present financial, environmental, and public health challenges. With water utility companies amassing vast amounts of data from enterprise information systems, the opportunity for data-driven leakage detection arises. With the increased use of enterprise information systems, water utility companies are generating vast amounts of data that can be valuable for predicting or detecting water leaks early and also for the development of new, automatic, and effective data-driven leak detection techniques. The presented study utilizes this data, by applying anomaly detection methods to heterogeneous time-series data from various components of the WDN in Slovakia. Our results demonstrate that components of the network, such as measured power consumption, water source temperature, and water source levels show significant positive associations with faults in the Water Distribution Network.
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