Smart Water and Digital Transition in Water Systems

Several papers in this Special Issue explore modeling and predictive analytics as essential tools for smart water management. In the face of increasing threats from extreme weather events, advanced modeling techniques and AI-based approaches play a pivotal role in predicting and mitigating adverse effects. For instance, Johnson et al. (2023) examined the data-driven modeling of municipal water system responses to hydroclimatic extremes. This type of modeling enables the anticipation of abrupt changes in water systems, facilitating effective responses to climate-related crises.

Another example (Kerimov et al. 2023) is the application of graph neural networks (GNNs), which evaluates the performance and transferability of metamodels in water distribution networks. This technique aids in understanding and predicting flow patterns and complex interactions within networks, a critical capability for optimal water system management. Similarly, Enriquez et al. (2023) leveraged black-box models based on AI to predict chlorine and trihalomethane (THM) concentrations, ensuring water quality and safety across supply networks.

The optimization of distribution networks forms another cornerstone of water system digitalization. Several articles in this Special Issue focus on enhancing infrastructure and network monitoring to improve operational efficiency and sustainability. For example, González & Saldarriaga (2023) compared traditional sewer system designs with optimized ones, highlighting the economic and sustainability benefits of applying optimization techniques to infrastructure design. This optimization contributes to cost reduction and minimizes environmental impacts, aligning with the objectives of smart water systems.

Diao et al. (2023) explored how centrality-guided optimization can enhance sensor placement in water distribution networks. This approach ensures strategic sensor placement, maximizing data capture efficiency and facilitating accurate real-time network monitoring. On the other hand, Barros et al. (2023) addressed leak detection in distribution networks using graph signal processing of pressure data. Early and effective leak detection reduces water losses and enhances system sustainability.

Regarding the water quality control, Vrachimis et al. (2024) focused on disinfection scheduling in water distribution networks, considering uncertainties in input time delays. The ability to forecast and adjust disinfection scheduling in real time is crucial for ensuring safe water supply. This underscores the importance of digitalized systems in quality management and risk control within distribution networks.

Advanced monitoring and emerging technologies for sustainability represent a key theme in the transition toward digitalized water systems. These approaches provide detailed and precise insights into water use and quality, which are vital for resource management and sustainable urban planning. In this context, Hassan et al. (2023) integrated drone (unmanned aerial vehicle (UAV))-based monitoring and sensor fusion to monitor solid waste landfills and detect water ponding issues. The application of such advanced technologies facilitates the early identification of potential environmental risks, contributing to the protection of water resources from contamination.

In the domain of domestic water consumption, Pavlou et al. (2024) and Mazzoni et al. (2024) addressed different approaches to monitoring and classifying residential water usage. Pavlou et al. (2024) compared model-based and data-driven methods for disaggregating water consumption into specific end uses, offering a detailed view of consumption patterns. Mazzoni et al. (2024), in turn, introduced an enhanced method for automated classification of household water data, leveraging AI tools to provide granular and automated insights into water use. Such monitoring aids better resource management and promotes sustainable consumption practices among users.

Additionally, Castro-Gama & Hassink-Mulder (2023) explored the optimization of charging station placement for autonomous robots in potable water networks. Autonomous robots represent a significant advancement in automated maintenance of water infrastructure, reducing operational costs while improving precision in network inspection and maintenance. The use of such technologies aligns closely with the goal of transitioning toward smart and sustainable water systems.

Finally, Xiang et al. (2024) focused on urban flood modeling through a two-dimensional hydrodynamic approach based on equivalent manhole drainage. This type of modeling is crucial in urban contexts, where the ability to predict and mitigate flooding is essential for ensuring the safety and resilience of cities. This study demonstrates how digitalization and advanced modeling empower water systems to respond proactively to extreme climatic events, protecting communities and minimizing infrastructure damage.

The collection of articles in this Special Issue provides an overview of the advances and challenges in digitalizing water systems, addressing topics ranging from predictive modeling and network optimization to advanced monitoring and emerging technologies. Each of these areas tackles fundamental aspects of the transition toward Smart Water and the management of water resources in the context of increasing climatic and demographic pressure. The WDSA–CCWI 2022 conference emphasized the importance of these innovations, and the articles presented here illustrate how digital technologies are transforming the water sector into a smarter, more resilient, and sustainable future.