With 120 Mio. m3 per year of lost water globally, leakages in drinking water distribution networks (WDN) still pose a major challenge to water utilities, furthermore, resulting in a multitude of cascading effects such as operational disruptions, environmental hazard, property damage, and sanitary issues. In the last decades there has been a growing focus on leakage detection within the scientific community leading to the development of numerous computer-based solutions for leakage detection. Despite these developments, practical approaches employed by water utilities in their leak management routines still primarily rely on in-situ acoustic devices in combination with periodic water audits, altogether falling short of ensuring continuous system monitoring and leaving much further potential for leakage reduction. Conclusively, further dissemination and widespread implementation of automatic leakage detection technology in the near future will be paramount to contain water losses and foster robust and climate-resilient water supply systems. Currently available computer-based technologies for leakage detection can be categorized either as data-driven or model-based, primarily depending on their requirement of a hydraulic model. Algorithms for leakage detection based on hydraulic models may accurately detect the occurrence and location of leakages, yet they are highly sensitive to model inputs and, thus, are required to be well calibrated. On the other hand, data-driven models operating on the premise of anomaly detection merely require data without any anomaly, i.e., leakage, for their calibration. However, these data-driven models cannot compete with the localisation accuracy of model-based leakage detection, as they do not incorporate geophysical information about the underlying WDN. Altogether, while yielding great improvement over in-situ technology, the requirements of automatic leakage detection technology still hamper its practical implementation. While both model-based and data-driven approaches have different requirements, their combination may ultimately enable mitigation of high technical requirements and, thus, enhance its practical applicability, thereby potentially facilitating a more efficacious, robust, and widespread implementation of leakage detection technology in water distribution networks. In this work, we explore the trade-off between model-based and data-driven leakage detection on the basis of two award-winning state-of-the-art leakage detection algorithms developed by our consortium in previous research, i.e., the data-driven LILA and the model-based Dual Model. Through the integration of both algorithms into a unified application, we aim to mitigate technical barriers and bolster detection robustness. To validate our approach, we quantitatively evaluate its performance regarding false alarms, time-to-detection, and localisation accuracy against the individual algorithms while considering different levels of confidence and availability regarding the input data, i.e., hydraulic model, water demand estimation, and pressure data.

Leakages in water distribution networks still pose major challenges to water utilities. Despite numerous technological advances, the adoption of digital leakage detection technology remains a slow process. Here, we present the project iOLE—intelligent Online LEakage detection, where we aim to increase the applicability of automated leak detection in practice through enhanced user experience and detection robustness. iOLE employs a human-centered design approach that involves the feedback of potential users during its development process to maximize subsequent user acceptance. To this end, we design a graphical user interface, combine model-based and data-driven leakage detection, and conduct a comprehensive robustness analysis.

Understanding the residential end uses of water is helpful for the sustainable management of water resources and the implementation of water conservation strategies. In this study, over one hundred studies were systematically reviewed to provide a comprehensive overview of the state-of-the-art research on end-use water consumption. Each study was reviewed, clustered, and subjected to a multilevel analysis aimed at quantitatively comparing the characteristics of the end uses of water available in the literature. The findings of this work support water utilities, researchers, policy makers, and consumers in identifying the key aspects of water end uses and exploring their main features across different geographical, socioeconomic, and cultural regions of the world.

This paper investigates the robustness of one innovative model-based method for leak detection, namely the Dual Model. We evaluate the algorithm’s performance under various leakage scenarios in the L-Town network, despite uncertainties and model mismatches in (i) base demand, (ii) pipe roughness, (iii) the number of sensors, and (iv) network topology. Our investigation results indicate that the Dual Model is highly sensitive to discrepancies in the first three parameters. However, the impact can be mitigated through sensor-specific calibration, such as adjusting sensor elevations. Moreover, the Dual Model has demonstrated robustness to minor topology mismatches, like those introduced by closed valves.

In recent decades, research on leak detection and localization in water distribution networks has been an area of growing interest in both water management and fault detection. In the literature, numerous leak localization techniques were developed from model-based methods (such as steady-state and quasi-steady state) and data-driven/machine learning models (e.g. time series modeling, prediction, and classification). However, there is still a need for study on the definition and enumeration of various sources, types, and nature of uncertainties in leak localization modelling processes. In the context of steady-state analysis, this review paper's main objective is to list the uncertainties related to model-based, data-driven and hybrid methods. This review outlines that, for the three classes of methods, the interplay of uncertainties with the modelling approximations jointly influences the localization performance and are often overlooked. Furthermore, realization of modelling assumptions and error propagation is needed for a successful real-world implementation.

Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applications in the design, control, and optimisation of water networks. Recent machine-learning-based metamodels grant improved fidelity and speed; however, they are only applicable to the water network they were trained on. To address this issue, we investigate graph neural networks (GNNs) as metamodels for WDSs. GNNs leverage the networked structure of WDS by learning shared coefficients and thus offering the potential of transferability. This work evaluates the suitability of GNNs as metamodels for estimating nodal pressures in steady-state EPANET simulations. We first compare the effectiveness of GNN metamodels against multi-layer perceptrons (MLPs) on several benchmark WDSs. Then, we explore the transferability of GNNs by training them concurrently on multiple WDSs. For each configuration, we calculate model accuracy and speedups with respect to the original numerical model. GNNs perform similarly to MLPs in terms of accuracy but take longer to execute but may still provide substantial speedup. Our preliminary results indicate that GNNs can learn shared representations across different networks, although assessing the feasibility of truly general metamodels requires further work.

A detailed characterization of residential water consumption is essential for ensuring urban water systems' capability to cope with changing water resources availability and water demands induced by growing population, urbanization, and climate change. Several studies have been conducted in the last decades to investigate the characteristics of residential water consumption with data at a sufficiently fine temporal resolution for grasping individual end uses of water. In this paper, we systematically review 114 studies to provide a comprehensive overview of the state-of-the-art research about water consumption at the end-use level. Specifically, we contribute with: (1) an in-depth discussion of the most relevant findings of each study, highlighting which water end-use characteristics were so far prioritized for investigation in different case studies and water demand modelling and management studies from around the world; and (2) a multi-level analysis to qualitatively and quantitatively compare the most common results available in the literature, i.e. daily per capita end-use water consumption, end-use parameter average values and statistical distributions, end-use daily profiles, end-use determinants, and considerations about efficiency and diffusion of water-saving end uses. Our findings can support water utilities, consumers, and researchers (1) in understanding which key aspects of water end uses were primarily investigated in the last decades; and (2) in exploring their main features considering different geographical, cultural, and socio-economic regions of the world.

Water utilities worldwide are under constant stress to reduce water loss due to urbanization, population growth, and climate change. Globally, Water Distribution Networks (WDNs) lose about 30% of the treated water on an average during supply. In addition to the amount of water lost, leaky WDNs consume additional energy and increase the risk of contamination. Deteriorating pipes and pipe network elements such as valves and joints, as well as improper pressure management are the main contributing factors for water loss in WDNs. Due to the increasing concern about water loss, leakage detection and localization have been widely researched in recent decades, both in continuously pumped and intermittently pumped systems. The techniques used for leakage detection and repair range from conventional methods with direct inspection on-site to model-based optimization methods. In the present era of low-cost sensors and the availability of high computing power, the transformation of WDNs into smart water systems is higher than ever. This has led to the research and development of data-driven and hybrid methods for solving leakage detection and localization methods. Irrespective of the class of methods used, their ultimate goal can be distilled primarily into two questions – a) How quickly and reliably can the presence of leak(s) be detected, and b) How accurate and precise can the location and size of the leak(s) be estimated? Answers to these questions include uncertainties inherent to the methods and models used, their underlying assumptions and necessary abstractions. Although much research has been done for many years to reduce uncertainties in leakage detection and localization, a comprehensive study using a consistent terminology of their types, sources, and effects on the outcome are missing. The main contribution of this work is to discuss (i) why there are uncertainties in the formulation of leakage detection and localization problem, (ii) identify the sources and types of uncertainties for different classes of modeling approaches (i.e., data-driven vs. model-based), and (iii) provide a brief review of their influence concerning error bounds from existing literature.

Global mean sea-level rise (SLR) has accelerated since 1900 from less than 2 mm/year during most of the century to more than 3 mm/year since 1993. At the regional scale, detection of an acceleration in SLR is difficult, because the long-term sea-level signal is obscured by large inter-annual variations with multi-year trends that are easily one order of magnitude larger than global mean values. Here, we developed a time series approach to determine whether regional SLR is accelerating based on tide gauge data. We applied the approach to eight 100-year records in the southern North Sea and detected, for the first time, a common breakpoint in the early 1990s. The mean SLR rate at the eight stations increases from 1.7±0.3 mm/year before the breakpoint to 2.7±0.4 mm/year after the breakpoint (95% confidence interval), which is unprecedented in the regional instrumental record. These findings are robust provided that the record starts before 1970 and ends after 2015.

Global mean sea-level rise (SLR) has accelerated since 1900 from less than 2 mm yr−1 during most of the century to more than 3 mm yr−1 since 1993. Decision-makers in coastal countries, however, require information on SLR at the regional scale, where detection of an acceleration in SLR is difficult, because the long-term sea-level signal is obscured by large inter-annual variations with multi-year trends that are easily one order of magnitude larger than global mean values. Here, we developed a time series approach to determine whether regional SLR is accelerating based on tide gauge data. We applied the approach to eight 100-year records in the southern North Sea and detected, for the first time, a common breakpoint in the early 1990s. The mean SLR rate at the eight stations increases from 1.7 ± 0.3 mm yr−1 before the breakpoint to 2.7 ± 0.4 mm yr−1 after the breakpoint (95% confidence interval), which is unprecedented in the regional instrumental record. These findings are robust provided that the record starts before 1970 and ends after 2015. Our method may be applied to any coastal region with tidal records spanning at least 40 years, which means that vulnerable coastal communities still have time to accumulate the required time series as a basis for adaptation decisions in the second half of this century.

Sensors used for wastewater flow measurements need to be robust and are, consequently, expensive pieces of hardware that must be maintained regularly to function correctly in the hazardous environment of sewers. Remote sensing can remedy these issues, as the lack of direct contact between sensor and sewage reduces the hardware demands and need for maintenance. This paper utilizes off-the-shelf cameras and machine learning algorithms to estimate the discharge in open sewer channels. We use convolutional neural networks to extract the water level and surface velocity from camera images directly, without the need for artificial markers in the sewage stream. Under optimal conditions, our method estimates the water level with an accuracy of ±2.48% and the surface velocity with an accuracy of ±2.08% in a laboratory setting—a performance comparable to other state-of-the-art solutions (e.g., in situ measurements).

<p>Water utilities tackle various problems in planning and operating their systems with complex and computationally expensive hydraulic models, i.e., maximizing system resilience, fault isolation, risk assessment, optimal pump scheduling, or water loss reduction via pressure management. To meet limited computational budgets, engineers employ less resource-intensive surrogate models. Current surrogate models based on artificial neural networks deliver similar accuracies as hydraulic models with lower computational costs. However, they require retraining when applied to an unknown water distribution system, which increases their computational load and limits their general applicability. Recent advancements in graph-based machine learning address these limitations. Graph neural networks (GNNs) naturally connect with the network elements (e.g., pipes and valves with edges, junctions, and tanks with vertices)  of water distribution systems, proving themselves to be a promising candidate for surrogate modeling. Once trained on a specific network to be a surrogate model, GNNs possess inductive biases that allow transferability to an unseen topology. In this work, we adopted a demand-driven simulation of a water distribution system in a graph machine learning setting. We built a synthetic dataset of demand-driven simulation with EPANET, founded on the example of real-world water distribution systems, and trained an attention-based GNN to emulate the hydraulic simulator. The accuracy was evaluated inductively on an unseen larger-sized water distribution network. We observed that the model showed promising transferability results to a larger network without the need for additional re-training on the unseen topology.</p>

The performance, vulnerability, and resilience of water distribution systems (WDS) are connected to its underlying topological structure (aka its shape). The literature mostly differentiates between two main shapes of networks - branched or looped. However, real networks come in various shapes and forms spanning between the two extremes of purely branched and looped types. Although these networks are globally topologically different, they may show high similarity on the local scale of a borough or a neighbourhood, or vice versa. Recent studies focused on describing WDS via graph theory representing pipes as edges and customers, tanks, and reservoirs as nodes, for example. The first attempt of graph theoretical applications showed promising results in estimating the global resilience of WDS, but there is a limited number of metrics that take the importance of local topology into consideration. Furthermore, iterative estimation of local vulnerability by simulating faults in each element of the system is prohibitively expensive from a computational point of view (i.e., various hydraulic simulations for assessing the vulnerability of each part of the system are needed).This research enters the new terrain of local WDS investigations through graphlet analysis. Graphlets are small connected subgraphs of a large network and have recently gathered much attention as a useful concept to describe local topology and uncover structural design principles of complex networks. Consequently, these novel analyses techniques can provide deep insights into how local WDS structures influence their overall performance.In this work, we investigate the influence of local network structures on the resilience of entire networks through graphlet analysis. First, we calculate local vulnerability of the network elements and global resilience indicators (i.e. Todini index, pipe and node criticality indices). We additionally simulate fault scenarios with EPANET and evaluate volumes of unsupplied demand on a multitude of real WDS. Second, we investigate the graphlet substructure of those WDSs to assess how much of the network’s vulnerability can be described by purely looking at the topology. First results show that graphlet representation of local neighbourhoods can serve as an efficient proxy metric capable of replacing computationally heavy performance analysis based on extensive hydraulic simulation. We additionally compare the influence of local changes on subgraphs to show how local changes in network design may grant improved robustness against such failures, ultimately increasing global resilience based on changing local topology. Urban water management can benefit from the proposed approach by not only identifying the most vulnerable elements of the critical infrastructure but providing insight into how to build globally more resilient WDS networks by enforcing small and therefore economical topological changes.

Tide gauges are the main source of information about sea-level changes in the Industrial Age. When looking at global mean values, century-long reconstructions produce rates between 1-2 mm/yr, while estimates over the last three decades reveal a much faster rise of about 3 mm/yr, as also indicated by satellite altimetry observations. In spite of this evidence for a recent acceleration, its quantification remains a challenging and relevant task, because results are highly dependent on the length of the record and on the reconstruction technique, whereas decision makers require clear proof to legitimise action. While global mean results are very important to understand climate change, regional to local variations are more relevant for the purpose of planning mitigation and adaptation measures. However, mainly due to natural variability, looking at individual tide gauge stations hampers the accurate determination of linear and non-linear trends. We developed a time series analysis framework to determine whether SLR is accelerating by detecting the presence of a break-point in the long-term trend. We applied the framework to tide gauges in the southern North Sea, where several stations provide high-quality and uninterrupted records spanning more than a century (1890-2018). On average, coastal sea level rise increased from ~1.8 mm/yr over the first century of our study to ~2.8 mm/yr over the last 25 years, albeit with large inter-station differences. We performed several tests to assess the impact of natural variability on the observed trend changes and verified that stable break-points are only detected since the late 1970s, with the exact year depending on the chosen time-window. Therefore, we believe that the high sea-level rates observed over the last few decades are likely representing long-term changes.

Socioeconomic characteristics arc influencing the temporal and spatial variability of water demand, which arc the biggest source of uncertainties within water distribution system modeling. Improving current knowledge of these influences can be utilized to decrease demand uncertainties. This paper aims to link smart water meter data to socioeconomic user characteristics by applying a novel clustering algorithm that uses a dynamic time warping metric on daily demand patterns. The approach is tested on simulated and measured single-family home data sets. It is shown that the novel algorithm performs better compared with commonly used clustering methods, both in finding the right number of clusters as well as assigning patterns correctly. Additionally, the methodology can be used to identify outliers within clusters of demand patterns. Furthermore, this study investigates which socioeconomic characteristics (e.g., employment status and number of residents) are prevalent within single clusters and, consequently, can be linked to the shape of the cluster's barycenters. In future, the proposed methods in combination with stochastic demand models can be used to fill data gaps in hydraulic models. (C) 2021 American Society of Civil Engineers.

Water utilities are challenged to reduce their water losses through detecting, localizing, and repairing leaks as quickly as possible in their aging distribution systems. In this work, we solve this challenging problem by detecting multiple leaks simultaneously in a water distribution network for the Battle of the Leak Detection and Isolation Methods. The performance of leak detection and localization depends on how well the system roughness and demand are calibrated. In addition, existing leaks affect the diagnosis performance unless they are identified and explicitly represented in the model. To circumvent this chicken-and-egg dilemma, we decompose the problem into multiple levels of decision-making (a hierarchical approach) where we iteratively improve the water distribution network model and so are able to solve the multileak diagnosis problem. First, a combination of time series and cluster analysis is used on smart meter data to build patterns for demand models. Second, point and interval estimates of pipe roughnesses are retrieved using least squares to calibrate the hydraulic model, utilizing the demand models from the first step. Finally, the calibrated primal model is transformed into a dual model that intrinsically combines sensor data and network hydraulics. This dual model automatically converts small pressure deviations caused by leaks into sharp and localized signals in the form of virtual leak flows. Analytical derivations of sensitivities with respect to these virtual leak flows are calculated and used to estimate the leakage impulse responses at candidate nodes. Subsequently, we use the dual network to (1) detect the start time of the leaks, and (2) compute the Pearson correlation of pressure residuals, which allows further localization of leaks. This novel dual modeling approach resulted in the highest true-positive rates for leak isolation among all participating teams in the competition. (C) 2021 American Society of Civil Engineers.