Abstract
Acoustic sensing technology is a familiar approach to detect leakage in urban water networks. Critical issues like false alarms, difficult leak locations, missed leaks, unknown site conditions, and high repair costs are still prevalent. The situation warrants developing a more sophisticated and efficient leak detection approach in real water networks. Hydrophone based acoustic technology has a strong promise for high precision detection of leaks. However, AIoT approach using hydroacoustic data for real water leak detection are rarely reported. The current study, therefore, proposes an integrated signal analysis and machine learning-based ensemble model for leak detection using a hydrophone-based smart IoT system. The results show that the most significant features are peak frequency and maximum amplitude. Random forest is the most robust classifier for cost effective long-term monitoring, and the proposed voting ensemble classifies leaks and no leaks with high accuracy on both unseen data and new sites. Specifically, proposed models have very few alarms and missed leaks are reported, a significant problem in models developed using accelerometers and noise loggers. The study shows a significant contribution to the domain of leak detection for real urban water networks.