Water losses are one of the main consequences of infrastructure failures in water distribution networks. While background leakages and pipe bursts in well maintained systems generally amount to only 3-7% of the total water supplied, they can account for more than 50% for poorly maintained networks worldwide. Methods that support prompt detection and accurate localization of leakages are crucial to help water utilities implement timely mitigation measures and avoid unnecessary loss of water.

Leakages can be classified as one type of anomaly occurring in water distribution networks. Broadly speaking, methods for their detection are referred to as anomaly detection methods. Anomaly detection methods have been studied extensively in the context of intrusion into information networks, and applied to water distribution networks in the similar context of cyber-attacks on SCADA systems. However, most current approaches for leakage detection rely on in-situ, engineering-based technology, while the development and application of data-driven approaches still poses several research challenges.

The goal of this project is to develop data-driven methods that are capable of detecting leakages in water distribution networks in real-time. As this research originated in an international competition, the BattLeDIM - Battle on Leakage Detection and Isolation Methods (http://battledim.ucy.ac.cy), its foundation is built upon the BattLeDIM dataset, inferring that the focus is put on the analysis of high resolution pressure data provided by a network of sensors located throughout the system. Data Mining and Machine Learning frameworks offer a wide range of opportunities for the analysis of this data and are comparatively utilized to identify and localize leakages as the primary type of anomaly.

The development of a data-driven methodology for leakage detection opens up the possibility to be extended to other applications in water distribution systems, including real-world systems, and assess their transferability to other problems where anomaly detection may be beneficial. The development of such an effective, decentralized framework implies the opportunity for additional research on IoT sensors, their communication interface, and their placement. Further research may be targeting wastewater systems to evaluate whether the developed methods may be cost-effectively transferred or adapted.