This work outlines the performance of three variants of deep neural networks for leak detection in water distribution networks, namely – autoencoders (AEs), variational autoencoders (VAEs), and long short-term memory autoencoders (LSTM-AEs). The multivariate pressure signals reconstructed from these models are analysed for leakage identification. The leak onset time is estimated using a fast approximation sliding window technique, which computes statistical discrepancies in prediction errors. The performance of all three variants is validated using the widely studied L-Town benchmark network. Furthermore, their feasibility for real-world application is studied by applying them to a real-world case study representing the data availability and network design often found in smaller- and medium-sized utilities in Norway. The results for the benchmark network showed that AE and LSTM-AE showed comparable detection performance for abrupt leaks with VAE performing the least. For incipient leaks, the LSTM-AE showed better detection performance with few false-positives. For the real-world dataset, the performance was significantly lower due to the quantity and quality of data available, and the contradiction of inherent requirements of data-driven models. In addition, the analysis revealed that the positioning of pressure sensors in the network is critical for the leak detection performance of these models.

This study outlines the use of a game theoretic approach for preliminary leak localization in water distribution networks. The proposed method consists of an autoencoder model at its core, trained to reconstruct input pressure signals recorded during nominal operation. Any significant change in the signal reconstructions is attributed to the presence of leaks and is determined by tracking statistical discrepancies using a sliding-window changepoint detection technique. Consequently, Shapley values are computed to identify the most influential sensors and approximate localization. With this approach, abrupt leaks were estimated within the 100 m radius and for incipient leaks at high flow rates.

Water utilities worldwide are under constant stress to reduce water loss due to urbanization, population growth, and climate change. Globally, Water Distribution Networks (WDNs) lose about 30% of the treated water on an average during supply. In addition to the amount of water lost, leaky WDNs consume additional energy and increase the risk of contamination. Deteriorating pipes and pipe network elements such as valves and joints, as well as improper pressure management are the main contributing factors for water loss in WDNs. Due to the increasing concern about water loss, leakage detection and localization have been widely researched in recent decades, both in continuously pumped and intermittently pumped systems.The techniques used for leakage detection and repair range from conventional methods with direct inspection on-site to model-based optimization methods. In the present era of low-cost sensors and the availability of high computing power, the transformation of WDNs into smart water systems is higher than ever. This has led to the research and development of data-driven and hybrid methods for solving leakage detection and localization methods. Irrespective of the class of methods used, their ultimate goal can be distilled primarily into two questions – a) How quickly and reliably can the presence of leak(s) be detected, and b) How accurate and precise can the location and size of the leak(s) be estimated?Answers to these questions include uncertainties inherent to the methods and models used, their underlying assumptions and necessary abstractions. Although much research has been done for many years to reduce uncertainties in leakage detection and localization, a comprehensive study using a consistent terminology of their types, sources, and effects on the outcome are missing. The main contribution of this work is to discuss (i) why there are uncertainties in the formulation of leakage detection and localization problem, (ii) identify the sources and types of uncertainties for different classes of modeling approaches (i.e., data-driven vs. model-based), and (iii) provide a brief review of their influence concerning error bounds from existing literature.