This study addresses complex multi-objective optimization challenges in large-scale, real-world water distribution networks (WDNs). The primary objectives are to improve a water quality index (water age) and network resilience by optimizing pipe diameters and network topology as decision variables. The proposed approaches leverage the non-dominated sorting genetic algorithm II (NSGA-II) producing Pareto optimal alternatives for water utility decision-makers. To enhance computational convergence runtime and solution quality of optimization, novel techniques are employed. These include advanced NSGA-II constraint handling, search space reduction, graph theory-based formulation of decision variables, constraints, and objective functions, as well as multi-stage and hydraulic-free optimization strategies. Furthermore, soft constraints are relaxed and integrated into Pareto decision-making spaces to provide a comprehensive, multi-criteria decision-making framework. The approaches are applied to a real case study, and the results demonstrate optimization performance improvements, with efficiency increasing by approximately 20% (in terms of convergence speed). Additionally, water age is reduced by 52% while achieving favorable results in the hydraulic and topological criteria. These findings highlight the effectiveness of the proposed methodologies in addressing WDN optimization challenges.

Most water utilities have to handle a substantial number of customer complaints every year. Traditionally, complaints are handled by skilled staff who know how to identify primary issues, classify complaints, find solutions, and communicate with customers. The effort associated with complaint processing is often great, depending on the number of customers served by a water utility. However, the rise of natural language processing (NLP), enabled by deep learning, and especially the use of deep recurrent and convolutional neural networks, has created new opportunities for comprehending and interpreting text complaints. As such, we aim to investigate the value of the use of NLP for processing customer complaints. Through a case study about the Water Utility Groningen in the Netherlands, we demonstrate that NLP can parse language structures and extract intents and sentiments from customer complaints. As a result, this study represents a critical and fundamental step toward fully automating consumer complaint processing for water utilities.

A digital twin (DT) is a virtual copy (a digital model) of a real system continuously fed with data to mimic the systems’ past, present and future behaviour. This makes it possible to detect anomalies, test new ideas and changes in the virtual system and assess how it reacts, minimizing the risks to the real system. In this sense, the DT can be seen as a playground to explore the effects of different scenarios and to practice how to best react and operate the physical system under these circumstances. The concept of DT has been used traditionally in the industry field1 but it can also be developed and exploited in a city management context, and in particular in Water Supply and Distribution Networks (WSDN), where it can be applied to all aspects of the system