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BattLeDIM: Battle of the Leakage Detection and Isolation Methods

Vrachimis; Eliades; Taormina; Ostfeld; Kapelan; Liu; Kyriakou; Pavlou; Qiu; Polycarpou

Drinking Water Distribution Networks (DWDN) are susceptible to infrastructure failures, which may lead to water losses. Typically, these water losses are due to background leakages and pipe bursts which may occur anywhere within the distribution network. Background leakages are normally difficult to detect due to their small size, whereas pipe bursts are easier to locate as they are of larger size and may appear on the surface. The early detection and localization of some leakage event is extremely important, as this would reduce the time required for accommodating the event and therefore reducing the risk of further infrastructure degradation, contamination events and consumer complaints. In previous years, a number of methodologies have been proposed to detect and isolate the location of leakage events using various types of sensor measurements. These methods were commonly evaluated on private commercial datasets, and as a result, it is not possible to objectively compare these methods in their ability to detect and isolate leaks. In the past year, a leakage detection dataset has been proposed, LeakDB, based on benchmark networks and created using the WNTR tool, using pressure-driven demands and realistic leakage modelling. Inspired by the “BATtle of the Attack Detection ALgorithms” (BATADAL), which focused on the detection of cyber-physical attacks, our team decided to organize a similar “battle” focusing on leakage events.
The Battle of the Leakage Detection and Isolation Methods (BattLeDIM),  aims at objectively comparing the performance of methods for the detection and localization of leakage events, relying on SCADA measurements of flow and pressure sensors installed within water distribution networks. Participants may use different types of tools and methods, including (but not limited to) engineering judgement, machine learning, statistical methods, signal processing, and model-based fault diagnosis approaches.

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