Leak localization in Water Distribution Networks By Directly Fitting the Learning Parameters of a Gaussian Naive Bayes Classifier

Water supply companies around the globe are struggling to meet the needs of an ever-increasing population, while climate change contributes to more drought. At the same time, up to 30% of the total amount of treated drinking water in the water supply system is lost due to leaks. An important strategy to reduce leak losses is using hydraulic modeling to localize leaks in a expert-driven manner. In this paper, we present a hybrid leak localization approach combining both hydraulic modeling and machine learning-based classification. A Gaussian Naive Bayes classifier is trained to localize leaks based on simulated pressures and historical pressure measurements. The simulated pressures are obtained using a hydraulic model of the water supply system. In our methodology, learned parameters of the classifier are inferred directly from processing the simulated and measured pressures, without the need for explicit training. We demonstrate the effectiveness of our leak localization approach by using real leak experiments, achieved by opening hydrants at different locations in an operational water supply system. State-of-the-art results are achieved, similar to an approach where explicit training is still needed.