1.13 Leakage detection in water distribution networks using AI and IoT
Challenge
In this work, we consider a set of real water pressure measurements from the water and wastewater company of Stockholm, Sweden (SVOA, Stockholm Vatten och Avfall). Specifically, we analyze the observed water pressure data collected from January 2018 to March 2019 in four selected pumping stations. These stations are located in a district-metered area (DMA) of the water distribution network (WDN). The DMA corresponds to a residential area that has a total population of 70,250 people.
Solution
We proposed a non-hydraulic modeling method for detecting leakage in WDNs through low-complexity learning strategies. Our proposed methodology uses learning strategies from unsupervised learning (K-means and cluster validation techniques), and supervised learning (learning vector quantization algorithms).
Impact on society
- Our algorithm can be applied to leakage detection scenarios where we have access to water pressure measurements at different points of the WDN.
- Our proposed methodology is suitable to real-world scenarios where measurements are available but without a detailed description of the architecture of the WDN.