The water sector is, currently and for the foreseeable future, challenged by rising levels of uncertainty in demand and availability of water, in a context of aging infrastructure and limited investment. In order to support strategic planning, water companies need a way to assess how their system behaves when faced with a range of changing conditions (climatic trends, asset deterioration, behavioral patterns, etc.) as well as accidents/incidents and/or extreme events (wildcards). In this study, a resilience assessment methodology was demonstrated, with ‘stress tests’ alternative water system configurations (including systems designed with decentralized or distributed philosophies) under a range of scenarios and extreme events. A ‘resilience profile graph’ was developed to quantify the performance of each configuration. The methodology was applied to the real-world urban water system of Oasen, which supplies the eastern part of the Province of South Holland, where the current system configuration and two potential future configurations were tested (one decentralized and one distributed). We show how the concept of resilience, operationalized through this methodology, can assist long term decision making and support strategic infrastructure planning.

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.

Despite global efforts to reduce non-revenue water (NRW), average levels remain at 30% worldwide and 25% in European countries. This paper investigates whether these persistent water losses stem primarily from uncontrollable environmental factors or from management practices. Through two rounds of questionnaires with water utilities across multiple countries and exploratory data analysis of data on 120 utilities, the research examines geographical conditions, utility characteristics, and water loss reduction strategies. Findings reveal that geographical conditions significantly impact Infrastructure Leakage Index (ILI) levels across Europe, suggesting that these factors should be considered when setting reduction targets. Also, a clear progression exists from low-resource measures implemented by high-ILI utilities to sophisticated technologies adopted by low-ILI utilities. While underinvestment likely contributes to high water losses, limited network investment data prevented a comprehensive analysis. The study recommends that the EU drinking water directive should account for utility size, establish measurement standards, encourage partnerships between large and small utilities, and include smaller utilities in reporting requirements. Additionally, incorporating economic metrics and investment data for pipe rehabilitation and digital technologies would enhance understanding of effective water loss reduction strategies. Comprehensive, standardized data collection could catalyze innovations to reduce ‘unavoidable’ losses and significantly decrease overall water losses across Europe.

Genetic algorithms can be a powerful tool for the automated design of optimal drinking water distribution networks. Fast convergence of such algorithms is a crucial factor for successful practical implementation at the drinking water utility level. In this technical note, we therefore investigate the performance of a suite of genetic variators that was tailored to the optimization of a least-cost network design. Different combinations of the variators are tested in terms of convergence rate and the robustness of the results during optimization of the real-world drinking water distribution network of Sittard, the Netherlands. The variator configurations that reproducibly reach the furthest convergence after 10⁵ function evaluations are reported. In the future these may aid in dealing with the computational challenges of optimizing real-world networks.

This paper proposes a new approach to rank a group of district metered areas (DMAs) in terms of background and unreported leakage rate and to quantify background/unreported leakage levels for individual DMAs in this group. This is done using an extension of the comparison of flow pattern distributions or CFPD method. The approach presented is based on an assumption of similarity in demand behavior between different DMAs. It requires no other data than net inflow timeseries for the DMAs or supply areas under consideration, and no assumptions other than that of similarity of demand. As such, it provides a low-data-requirements method for the evaluation of background and unreported leakage that does not share underlying assumptions with the commonly used mininum night flow method and may potentially present a supplement or alternative to it. The approach is validated using numerical simulations and applied to flow data of a set of DMAs from the Netherlands.

Purpose: European Union (EU) research projects generally involve international teams based in different countries. This means that researchers need to travel internationally to participate for in-person meetings, which are crucial for facilitating collaboration among research teams and provide a platform for teams to share their progress. Unfortunately, much of the international traveling in EU projects is done by air and therefore it has a significant carbon footprint. One potential solution that has gained attention in recent years is virtual reality (VR) and the metaverse. The aim of this work is to investigate to what degree VR meetings provide a viable alternative to physical meetings in the context of EU research projects and can thus contribute to climate change mitigation.Design/methodology/approach: A three-stepped approach was chosen. First, the requirements for VR meetings were determined by collecting all relevant aspects of physical meetings through a questionnaire. Second, a set of VR meeting experiments were designed and executed. Third, carbon footprints for both physical traveling and VR meetings (lifecycle) were calculated.Findings: We conclude that VR can be a powerful tool which can supplement international projects and mitigate carbon emissions associated with traveling for in-person meetings by an estimated 7–19 times.Originality/value: This paper explores the suitability of the current generation of VR technology and quantitatively evaluates its effectiveness for greenhouse gas emissions mitigation in the context of a European research project.