Water Leak Detection via Domain Adaptation

Outdated infrastructure contributes to significant water wastage, where leaks can represent as much as 30% of urban water supply losses. Rapid and precise leak detection is therefore crucial for economic and environmental reasons. Data-driven methods have emerged as promising solutions to detect water leaks due to their accurate performance. However, they encounter obstacles like limited labeled datasets and adapting to various situations. To address these challenges, we explore Semi Supervised Learning (SSL) and Transfer Learning (TL) techniques in the context of water leak detection. We propose to address the problem of leak detection in case of limited labeled data, using a Convolutional Neural Network (CNN) trained on a laboratory-scale network, then adapted to work on real data. To do so, we compare three different domain adaptation techniques that leverage only a small amount of data from the new domain. Our results show that Self-Tuning techniques proves better than the others for this task, even with limited data.