Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses through leak detection and localization, timely and efficiently using advanced techniques is an urgent humanitarian need. There are numerous methods being used to localize water leaks in WDNs through constructing hydraulic models or analyzing flow/pressure deviations between the observed data and the estimated values. However, from the application perspective, it is very practical to implement an approach which does not rely too much on measurements and complex models with reasonable computation demand. Under this context, this paper presents a novel method for leak localization which uses a data-driven approach based on limit pressure measurements in WDNs with two stages included: (1) Two different machine learning classifiers based on linear discriminant analysis (LDA) and neural networks (NNET) are developed to determine the probabilities of each node having a leak inside a WDN; (2) Bayesian temporal reasoning is applied afterwards to rescale the probabilities of each possible leak location at each time step after a leak is detected, with the aim of improving the localization accuracy. As an initial illustration, the hypothetical benchmark Hanoi district metered area (DMA) is used as the case study to test the performance of the proposed approach. Using the fitting accuracy and average topological distance (ATD) as performance indicators, the preliminary results reaches more than 80% accuracy in the best cases.

The urban water cycle (UWC), which is composed of the water supply system (WSS) and urban drainage system (UDS), is a critical infrastructure required for the functioning of urban society. Considering the growing pollution and subsequent water scarcity caused by increasing urbanization and climate change, efficient UWC management is required to maintain resource sustainability and environmental protection. Cyber-physical systems (CPSs) provide a technological suite for the efficient management of critical systems. To exploit advantages of CPS for UWC, this paper proposes a CPS-based management framework enabling supervision, subsystem interoperability, and integrated optimization of UWC: (1) Firstly, clear definitions are provided to demonstrate that UWC systems can be considered as CPSs. (2) A multi-layer CPS-based supervision framework is presented afterwards, conceptually dividing the physical UWC and its digital counterpart into Supervision&Control, Scheduling, Digital Twin, and Water Users and Environment four layers. (3) The information flows that interact with each layer, as well as a key aspect of CSP operation, namely the interoperability among subsystems in the context of UWC, are also addressed. (4) To demonstrate advantages of supervision and interoperability of subsystems under the CPS framework, an integrated optimizer based on model predictive control (MPC) is applied and compared against the individual control of each system. A real case study of the WSS and UDS in Barcelona UWC is applied in order to validate the proposed approaches through virtual reality simulations based on MATLAB/SIMULIN and EPA-SWMM.