Water is an essential resource for life and is also a necessary resource for the sustainable economic competitiveness of any country. In recent decades, climate change, economic development, and rising population have led to water scarcity in certain regions. In response, new technologies and water management techniques have been researched and developed, which are now incorporated into the concept of smart cities. These innovations, called smart water systems, aim to enhance water management by monitoring consumption, quality, reservoir levels, leaks, and asset conditions, and optimizing water processes to maximize water system resilience. The first systems were based on smart meters and have advanced to so-called digital twins for water systems. This review aims to present a comprehensive review of smart water system developments, the geographic distribution of the works, their technological readiness level, and their implementation challenges.

A novel discontinuous observer design for nonlinear systems, whether stable or not, is presented in this paper; it removes former limitations on matching and minimum-phase conditions, Lipschitz bounds, and linearity of outputs. Based on a pair of diffeomorphisms an observer error system can be written whose nonlinear nominal model depends entirely on the error, input, and output signals, thus allowing error-dependent domains of attraction where unstable signals are estimated. Nonlinear nominal models constitute the key for the proposal power and flexibility; they are handled via convex embedding of nonlinearities and the direct Lyapunov method. The discontinuous nature of the observer makes it insensitive to matched signals; the influence of unmatched terms is attenuated via $\mathscr {H}_\infty$ techniques. All design conditions are in the form of linear matrix inequalities. The examples illustrate the advantages over the existing methodologies even for unstable non-minimum-phase systems that do not hold the standard matching or Lipschitz conditions.