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The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. With adverse industrial effects on the global landscape, climate change is imploring the global economy to adopt sustainable solutions. The ongoing evolution of energy efficiency targets massive data collection and Artificial Intelligence (AI) for big data analytics. Besides, emerging on the Internet of Energy (IoE) paradigm, edge computing is playing a rising role in liberating private data from cloud centralization. In this direction, a creative visual approach to understanding energy data is introduced. Building upon micro-moments, which are time-series of small contextual data points, the power of pictorial representations to encapsulate rich information in a small twodimensional (2D) space is harnessed through a novel Gramian Angular Fields (GAF) classifier for energy micro-moments. Designed with edge computing efficiency in mind, current testing results on the ODROID-XU4 can classify up to 7 million GAF-converted data points with ~90% accuracy in less than 30 sec, paving the path towards industrial adoption of edge IoE.

Urban water infrastructures are an essential part of urban areas. For their construction and maintenance, major investments are required to ensure an efficient and reliable function. Vital parts of the urban water infrastructures are water distribution networks (WDNs), which transport water from the production (sources) to the spatially distributed consumers (sinks). To minimize the costs and at the same time maximize the resilience of such a system, multi-objective optimization procedures (e.g., meta-heuristic searches) are performed. Assessing the hydraulic behavior of WDNs in such an optimization procedure is no trivial task and is computationally demanding. Further, deciding how close to optimal design solutions the current solutions are, is difficult to assess and often results in an unnecessary extent of experiment. To tackle these challenges, an answer to the questions is sought: when is an optimization stage achieved from which no further improvements can be expected, and how can that be assessed? It was found that graph characteristics based on complex network theory (number of dual graph elements) converge towards a certain threshold with increasing number of generations. Furthermore, a novel method based on network topology and the demand distribution in WDNs, specifically based on changes in ‘demand edge betweenness centrality’, for identifying that threshold is developed and successfully tested. With the proposed novel approach, it is feasible, prior to the optimization, to determine characteristics that optimal design solutions should fulfill, and thereafter, test them during the optimization process. Therewith, numerous simulation runs of meta-heuristic search engines can be avoided.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. The notion of employing a Deep Learning (DL) for gas classification has kindled revolution in the field that has both improved data collection measures and classification performance. Yet, the current literature, with its vast contributions, has potential in enhancing the current state-of-the-art by employing both DL and novel visualization methods to boost classification performance and speed. Therefore, this paper presents a dual classification system for high-performance gas classification: on 1D time series data and on 2D Gramian Angular Field (GAF) data. For the GAF case, 1D data is converted into 2D counterparts by means of normalization, segmentation, averaging, and color-coding. The Gas Sensor Array (GSA) dataset is used for evaluating the implemented AlexNet model for classifying 2D GAF data and an improved version of GasNet for 1D time-based data. Using a cloud-based architecture, the two models are evaluated and benchmarked with the state-of-the-art. Evaluation results of the modified GasNet model on time series data signifies state-of-the-art accuracy of 96.0%, while AlexNet achieved 81.3% test accuracy of GAF classification with near real-time performance on edge computing platforms.

This dataset, collected in 2022 in a domestic household in the UK, provides appliance-level power consumption data and ambient environmental conditions as a timeseries and as a collection of 2D images created using Gramian Angular Fields (GAF). The importance of the dataset lies in (a) providing the research community with a dataset that combines appliance-level data coupled with important contextual information for the surrounding environment; (b) presents energy data summaries as 2D images to help obtain novel insights from the data using data visualization and Machine Learning (ML). The methodology involves installing smart plugs to a number of domestic appliances, environmental and occupancy sensors, and connecting the plus and the sensors to a High-Performance Edge Computing (HPEC) system to privately store, pre-process, and post-process data. The heterogenous data include several parameters, including power consumption (W), voltage (V), current (A), ambient indoor temperature (°C), relative indoor humidity (RH%), and occupancy (binary). The dataset also includes outdoor weather conditions based on data from MET Norway including temperature (°C), outdoor humidity (RH%), barometric pressure (hPA), wind bearing (deg), and windspeed (m/s). This dataset is valuable for energy efficiency researchers, electrical engineers, and computer scientists to develop, validate, and deploy and computer vision and data-driven energy efficiency systems.

The file attached to this record is the author's final peer reviewed version. Pipe re-sizing of Water Distribution Networks (WDNs) aims at improving the service performance to the required level, while minimizing the cost of replacing pipes in the network. The main challenge comes from the identification of the most effective pipes to re-size from a large number of interacting components. Performing a global search over all pipes in large WDNs does not guarantee to obtain a feasible and efficient solution due to the enormous search space, even by employing advanced algorithms, e.g., evolutionary algorithms. This paper proposes a novel method to reduce the search space for optimal re-sizing based on topological metrics from Complex Network Theory and hydraulic metrics, while providing sub-optimal solutions comparable to the full search solutions, i.e., considering all pipes as candidates. The topological metrics are based on the edge-betweenness tailored for WDN analysis. Hydraulic metrics are unit headloss and flow rates of pipes computed based on simulation of the WDN in the current configuration. The optimal re-sizing plans obtained, particularly that using edge betweenness were tested on a real WDN. The results are comparable with the full search solutions but they are much more efficient to obtain and require replacing mostly contiguous pipes, i.e., easier for practical fieldwork.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Fractals have been identified as a common feature in many natural and artificial networks that exhibit self-similarity of the topological patterns, i.e. different parts of the system have similar structures to each other as well as to the whole system. This study investigates the fractality in water distribution networks (WDNs) and the application of the fractal property in WDNs analysis. Specifically, we explore the existence of fractal topological patterns in eight real-world WDNs of different complexities by using the box-covering algorithm. The results demonstrate all of the studied WDNs are fractal. Moreover, the application of the fractal property is demonstrated via critical pipe identification and optimal rehabilitation of benchmark real-world WDNs. All results show that the fractal-based approach can achieve better or equally good solutions compared with conventional methods in a much more efficient way, e.g. via automation of some processes or significant reduction in the search space/components to consider.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Complexity in water distribution systems (WDSs) poses a challenge for analysis and management of the systems. To reduce the complexity, the recent development of complex network science provides a system decomposition technique that converts a complex WDS with a large number of components into a simple system with a set of interconnected modules. Each module is a subsystem with stronger internal connections than external connections. Thus far, the topological features of the modular structure in WDS have been extensively studied but not the behavioural features, e.g. the hydraulic interdependencies among modules. Therefore, this paper aims to quantitatively measure and graphically visualize the module interdependency in WDSs, which helps understanding the behavioural complexity of WDSs and thus various WDS analyses, such as pipe maintenance, model calibration, rehabilitation, and District Metered Areas planning. Specifically, this study first identifies the WDS’s modular structure then measures how changes in the state of one module (i.e. any single pipe failure or perturbed demand within each module) affect the state of another module. Modular interdependencies are summarized in an interdependency matrix and visualized by the digraph. Four real-world systems are analysed, and three of them shows low interdependencies among most of the modules and there are only a few critical modules whose status changes will substantially affect a number of other modules. Hence, highly interconnected topologies may not result in strong and complex module interdependency, which is a fact that simplifies several WDS analysis for practical applications as discussed in this paper.

This paper shares a vision that sustainable water supply requires resilient water infrastructures which are presumably in the centralized control and decentralized execution (CCDE) mode with multiscale resilience. The CCDE should be planned based on the multiscale structure of water infrastructures, in which the systems are divided into a number of hierarchically organized subsystems. The CCDE allows independent execution of all subsystems under normal situations yet coordination of subsystems at different scales to mitigate any disturbances during failure events, i.e. the multiscale resilience. This vision is discussed in detail for water distribution systems (WDSs). Specifically, the conceptual design of the multiscale CCDE is described, and progress on understanding the multiscale structures in WDSs is summarized based on the literature review. Furthermore, a few theories consistent with the multiscale CCDE concept are discussed which include the decomposition theorems, fractal theory, control theories, and complex network theory. The next step in the vision will be to identify the optimal multiscale structure for the CCDE based on the best trade-off of different goals of WDS analysis and management. This process needs support from not only innovative modelling tools and extensive datasets and theories but also inspiring exemplar systems, e.g. natural systems. HIGHLIGHTS Centralized control and decentralized execution (CCDE) is proposed as a form for resilient water infrastructures.; Planning CCDE based on multiscale structures in urban water systems realizes multiscale resilience.; The design of CCDE is illustrated via a real-world water distribution system (WDS).; Extensive studies on multiscale structures in WDSs are summarized.; Theoretical supports for CCDE are identified.

Resilience as a concept and resilience assessment as a practice are being explored across a range of social, ecological and technical systems. In this paper, we propose a new method and visualisation approach for interrogating the communication of resilience within organisational networks, using participatory social network analysis and message passing. Through an examination of the UK water sector organisational network, represented by multiple co-produced network graphs, we identify organisations having a key role in the communication of resilience regulatory and evidence messages, as well as highlighting the potential role of complexity tools in strategy formulation. Animations are presented showing the dynamics of resilience communication, which is discussed. Reflections on the use of participatory social network analysis are explored, as the method opens new doors to potentially examine how network changes could alter communication. Key insights highlight that perceived responsibilities for resilience in the UK water sector rest with a small core of organisations; water customers play a limited role in the two-way communication of resilience and water sector organisations do not communicate widely on resilience with other sectors (such as energy). Additionally, who an organisations’ neighbours are and what catalyses a message to be passed are important in determining how quickly messages spread. Results lead to a recommendation that high level governmental and policy organisations should engage to a greater extent with new resilience knowledge and consider the use of complexity tools in policy making. Policy in relation to resilience is not keeping pace with such knowledge, limiting the communication and learning of organisations who ardently follow policy and regulation. For inter-organisational cooperation to make a difference to water governance, such organisations need to be encouraged to communicate and embed the latest approaches in relation to resilience and complexity thinking and practice., Animation; Governance; Policy; Resilience; Social network analysis; Water; Data visualization; Green engineering; Natural resource management; Network analysis; Knowledge representation.

open access article Short-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 minute time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases.

open access article Multiscale resilience, i.e., coordinating different scales within a system to jointly cope and mitigate risks on any single scale, is identified as the feature of a complex resilient system. However, in water distribution systems (WDSs) and urban drainage systems (UDSs), the inherent resilience is usually not multiscale resilience. By referring to the larger scale to larger pipes serving both local users and some other users at smaller scales, it can be found that smaller scales are not responsible for providing resilience to cope with failures in larger scales. These are because the main function of traditional water systems is to deliver water from upstream to downstream. This study demonstrates that improving multiscale resilience in WDSs and UDSs needs to allow water to travel reversely in the system via providing extra capacities and/or connections at smaller scales. This hypothesis is verified via case studies on a real world WDS and UDS.

The reliable functioning of water infrastructures is one of the key pillars for society, and it is crucial for social well-being and supports economic growth [...]

International audience; The paper presents the results of the Battle of Postdisaster Response and Restoration (BPDRR) presented in a special session at the first International water distribution systems analysis & computing and control in the water industry (WDSA/CCWI) Joint Conference, held in Kingston, Ontario, Canada, in July 2018. The BPDRR problem focused on how to respond and restore water service after the occurrence of five earthquake scenarios that cause structural damage in a water distribution system. Participants were required to propose a prioritization schedule to fix the damages of each scenario while following restrictions on visibility/nonvisibility of damages. Each team/approach was evaluated against six performance criteria: (1)time without supply for hospital/firefighting, (2)rapidity of recovery, (3)resilience loss, (4)average time of no user service, (5)number of users without service for eight consecutive hours, and (6)water loss. Three main types of approaches were identified from the submissions: (1)general-purpose metaheuristic algorithms, (2)greedy algorithms, and (3)ranking-based prioritizations. All three approaches showed potential to solve the challenge efficiently. The results of the participants showed that for this network, the impact of a large-diameter pipe failure on the network is more significant than several smaller pipes failures. The location of isolation valves and the size of hydraulic segments influenced the resilience of the system during emergencies. On average, the interruptions to water supply (hospitals and firefighting) varied considerably among solutions and emergency scenarios, highlighting the importance of private water storage for emergencies. The effects of damages and repair work were more noticeable during the peak demand periods (morning and noontime) than during the low-flow periods; and tank storage helped to preserve functionality of the network in the first few hours after a simulated event.

Resilient water distribution systems (WDSs) need to minimize the level of service failure in terms of magnitude and duration over its design life when subject to exceptional conditions. This requires WDS design to consider scenarios as close as possible to real conditions of the WDS to avoid any unexpected level of service failure in future operation (e.g., insufficient pressure, much higher operational cost, water quality issues, etc.). Thus, this research aims at exploring the impacts of design flow scenarios (i.e., spatial-variant demand patterns) on water distribution system design and operation. WDSs are traditionally designed by using a uniform demand pattern for the whole system. Nevertheless, in reality, the patterns are highly related to the number of consumers, service areas, and the duration of peak flows. Thus, water distribution systems are comprised of distribution blocks (communities) organized in a hierarchical structure. As each community may be significantly different from the others in scale and water use, the WDSs have spatially variable demand patterns. Hence, there might be considerable variability of real flow patterns for different parts of the system. Consequently, the system operation might not reach the expected performance determined during the design stage, since all corresponding facilities are commonly tailor-made to serve the design flow scenario instead of the real situation. To quantify the impacts, WDSs&rsquo, performances under both uniform and spatial distributed patterns are compared based on case studies. The corresponding impacts on system performances are then quantified based on three major metrics, i.e., capital cost, energy cost, and water quality. This study exemplifies that designing a WDS using spatial distributed demand patterns might result in decreased life-cycle cost (i.e., lower capital cost and nearly the same pump operating cost) and longer water ages. The outcomes of this study provide valuable information regarding design and operation of water supply infrastructures, e.g., assisting the optimal design.

Seismic performance assessment of water distribution systems (WDSs) based on hydraulic simulation is essential for resilience evaluation of WDSs under earthquake disasters. The assessment is mainly to determine how the water supply will be affected due to pipe breaks caused by the earthquake, with the water supply loss estimated based on the loss of supply to nodes. Existing research works usually use the average or overall performance metric of all user nodes as the system performance indicator without considering user nodes' individual performance and criticality. This paper proposes a framework to evaluate the importance of user nodes considering post-earthquake rescue service and the seismic performance of individual user nodes in the WDS, which supports the pipeline renovation plan to improve the performance of critical user nodes. The importance of user nodes is evaluated by a multi-index model, including the indices for daily service, post-earthquake rescue service, and network topology influence of user nodes. These indices evaluate the importance of user nodes in terms of their roles for daily water service, emergent rescue service, and water transmission to other nodes, respectively. Fragility model of pipelines evaluates the earthquake-induced damages of the WDS, and the seismic performance assessment of the WDS system is performed by the hydraulic model of the WDS with pipeline damages. The proposed framework is implemented in an actual WDS, the results show that the importance classification to user nodes by multi-index approach can identify the critical user nodes for post-earthquake rescue service, which traditional methods may ignore. The importance classification and seismic performance of individual user nodes make it feasible to check the seismic performance of critical user nodes and formulate a targeted pipeline renovation plan to focus limited resources on critical user nodes.

Evaluating and enhancing resilience in water infrastructure is a crucial step towards more sustainable urban water management. As a prerequisite to enhancing resilience, a detailed understanding is required of the inherent resilience of the underlying system. Differing from traditional risk analysis, here we propose a global resilience analysis (GRA) approach that shifts the objective from analysing multiple and unknown threats to analysing the more identifiable and measurable system responses to extreme conditions, i.e. potential failure modes. GRA aims to evaluate a system's resilience to a possible failure mode regardless of the causal threat(s) (known or unknown, external or internal). The method is applied to test the resilience of four water distribution systems (WDSs) with various features to three typical failure modes (pipe failure, excess demand, and substance intrusion). The study reveals GRA provides an overview of a water system's resilience to various failure modes. For each failure mode, it identifies the range of corresponding failure impacts and reveals extreme scenarios (e.g. the complete loss of water supply with only 5% pipe failure, or still meeting 80% of demand despite over 70% of pipes failing). GRA is also able to illustrate the similarities or differences in responses of different systems to various failure modes. Moreover, GRA reveals that increased resilience to one failure mode may decrease resilience to another and the same fraction of component failure could result in tremendously different level of failure impacts. The method can be used as a comprehensive diagnostic framework to evaluate a range of interventions for improving system resilience in future studies.

Global threats such as climate change, population growth, and rapid urbanization pose a huge future challenge to water management, and, to ensure the ongoing reliability, resilience and sustainability of service provision, a paradigm shift is required. This paper presents an overarching framework that supports the development of strategies for reliable provision of services while explicitly addressing the need for greater resilience to emerging threats, leading to more sustainable solutions. The framework logically relates global threats, the water system (in its broadest sense), impacts on system performance, and social, economic, and environmental consequences. It identifies multiple opportunities for intervention, illustrating how mitigation, adaptation, coping, and learning each address different elements of the framework. This provides greater clarity to decision makers and will enable better informed choices to be made. The framework facilitates four types of analysis and evaluation to support the development of reliable, resilient, and sustainable solutions: “top‐down,” “bottom‐up,” “middle based,” and “circular” and provides a clear, visual representation of how/when each may be used. In particular, the potential benefits of a middle‐based analysis, which focuses on system failure modes and their impacts and enables the effects of unknown threats to be accounted for, are highlighted. The disparate themes of reliability, resilience and sustainability are also logically integrated and their relationships explored in terms of properties and performance. Although these latter two terms are often conflated in resilience and sustainability metrics, the argument is made in this work that the performance of a reliable, resilient, or sustainable system must be distinguished from the properties that enable this performance to be achieved.

In future cities, urban water systems (UWSs) should be designed not only for safe provision of services but should also be resilient to emerging or unexpected threats that lead to catastrophic system failure impacts and consequences. Resilience can potentially be built into UWSs by implementing a range of strategies, for example by embedding redundancy and flexibility in system design, or by rehabilitation to increase their ability to maintain acceptable customer service levels during unexpected system failures. In this work, a new resilience analysis is carried out to investigate the performance of a water distribution system (WDS) and an urban drainage system (UDS) during pipe failure scenarios. Using simplified synthetic networks, the effect of implementing adaptation (resilient design) strategies on minimising the loss of system functionality and cost of UWSs is investigated. Study results for the WDS case study show that the design strategy in which flexibility is enhanced ensures that all customers are served during single pipe failure scenarios. The results of the UDS case study indicate that the design strategy incorporating upstream distributed storage tanks minimises flood volume and mean duration of nodal flooding by 50.1% and 46.7%, respectively, even when system functionality is significantly degraded. When costs associated with failure are considered, resilient design strategies could prove to be more cost-effective over the design life of UWSs.

Water quality sensors placed in water distribution systems (WDSs) are critical for detecting accidental or intentional contamination intrusion. This motivates research to optimally place a limited number of sensors for a given WDS aimed to maximize the detection effectiveness (e.g., the detection likelihood and time to detection). Typically, effectiveness of a sensor placement strategy (SPS) is assessed using the expected impact across a number of contamination scenarios. Despite the value of such an approach, it may provide limited information on the SPS’s comprehensive properties in detection, such as the SPS’s ability to detect events with different levels of consequences, or to reduce the impacts from undetectable events. To address this limitation, this study investigates the underlying characteristics of the SPS’s effectiveness for contamination detection using a set of metrics focusing on detection time, consumption of contaminated water, the number of contaminated demand nodes, and the contaminated spatial distance. The former two are derived from detectable contamination scenarios and the latter two are computed from both detectable and undetectable contamination scenarios. The proposed method is illustrated for two real-world WDSs, and the results reveal the underlying properties of the SPS’s utility in contamination detection, which is significantly more informative than the measure of expected impacts. Such improved understanding provides guidance for selecting the most appropriate SPS and for improving preparedness for contamination events.

The Schur complement domain decomposition method is used for solution of large linear systems. The algorithm is based on the subdivision of the domain into smaller ones and the solution of those sub-domains independently. Regarding water distribution systems modeling, the hydraulic simulation could be formulated as a sequence of systems of linear equations. Therefore, this paper utilizes the domain decomposition method to accelerate the simulation process further. The method is evaluated using a large scale real-world system with 63,616 junctions and 64,200 pipes as case study. The case study shows that the methodology could improve the performance of hydraulic simulation app. by a factor of 8 without losing accuracy at a suitable level of domain decomposition. Although the optimal level of decomposition is case specific, considerable speedup might still be achievable by decomposing a large system into only a few subsystems. We utilizes a domain decomposition method to speed up water distribution simulation.The author's method is verified based on a large scale real world water distribution system.Compared with EPANET, the method could accelerate hydraulic simulation app. by a factor of 8 without losing accuracy.The method is general applicable to all solution methods of water distribution simulation.

Fractals have been identified as a common feature of many natural and artificial systems that exhibit similar patterning at different scales. Understanding fractals is a critical aspect of decoding complex systems, as the pattern of such large systems can be revealed by identifying only a small part of the system. Furthermore, identify existing features of such systems can start at the large scale with the fewest details of the system under scrutiny before doing a more detailed analysis at finer scales. Such a process provides an efficient and reliable way of analysing and managing information of big data systems. This study revealed the fractality in water distribution networks (WDNs) based on research on fractals in complex networks. Specifically, we explored the existence of fractal patterns in six real world WDNs of different complexities (e.g. from a network with only 21 pipes to a network with 2465 pipes). The box-covering algorithm has been applied, which is the most widely used method to distinguish between fractal or non-fractal networks. The WDNs are first mapped into undirected graphs. Next, the method partitions the nodes into boxes of size l B , i.e. the maximal distance between nodes within each box is at most l B − 1 . By varying the box sizes, different minimum numbers of boxes N B required to cover the entire network can be identified. A network is fractal if the regression line for log ( N B ) and log ( l B ) is linear. The results demonstrate the existence of fractal patterns in all case study WDNs, as linear regression lines with coefficient of determination over 0.95 ( R 2 > 0 . 95 ) are obtained in all analyses. As further verification, the self-similarity on multiscales is revealed, i.e. the similarity in patterns of component criticality. Based on the fractal patterns, a systematic method is also developed for more efficient identification of critical pipes in WDNs, e.g. reducing the computational load by 61% in the case study.

A patent has been authorized for the method developed. -Dr Tom Walski, a top expert in the water industry, has given positive feedback. -Two project proposals have been submitted in collaboration with leading companies. -Dr. Diao has been invited by Severn Trent Water, Shenzhen Water Utility, and Tsinghua Holdings Human Settlements Environment Institute to present the method and discuss possible collaborations. Accounting for water in a distribution system can be improved by dividing systems into smaller, metered zones. This paper proposes an approach that could create boundaries for district metered areas (DMA) automatically on the basis of the community structure of water distribution systems. Community structure—the gathering of vertices into communities such that there is a higher density of edges within communities than between them—is a common property of many complex systems. For verification, the method was tested on a real-world distribution system, and the result was compared with a manually designed DMA layout. Although further improvements are necessary, because the achieved community structure is in excellent agreement with the zoning plan in reality, this approach is a new addition to the number of automated methods aimed at complementing and eventually substituting the empirical trial-and-error approach.

Calibration is a process of comparing model results with field data and making the appropriate adjustments so that both results agree. Calibration methods can involve formal optimization methods or manual methods in which the modeler informally examines alternative model parameters. The development of a calibration framework typically involves the following: (1) definition of the model variables, coefficients, and equations; (2) selection of an objective function to measure the quality of the calibration; (3) selection of the set of data to be used for the calibration process; and (4) selection of an optimization/manual scheme for altering the coefficient values in the direction of reducing the objective function. Hydraulic calibration usually involves the modification of system demands, fine-tuning the roughness values of pipes, altering pump operation characteristics, and adjusting other model attributes that affect simulation results, in particular those that have significant uncertainty associated with their values. From the previous steps, it is clear that model calibration is neither unique nor a straightforward technical task. The success of a calibration process depends on the modeler’s experience and intuition, as well as on the mathematical model and procedures adopted for the calibration process. This paper provides a summary of the Battle of the Water Calibration Networks (BWCN), the goal of which was to objectively compare the solutions of different approaches to the calibration of water distribution systems through application to a real water distribution system. Fourteen teams from academia, water utilities, and private consultants participated. The BWCN outcomes were presented and assessed at the 12th Water Distribution Systems Analysis conference in Tucson, Arizona, in September 2010. This manuscript summarizes the BWCN exercise and suggests future research directions for the calibration of water distribution systems.

This paper proposes a methodology to decompose water distribution systems (WDSs) into a twin-hierarchy pipeline structure consisting of backbone mains and community feeders. The method has three steps: (1) clustering analysis; (2) vulnerability analysis; and (3) identification of backbone mains and community feeders. After decomposition, analysis of a whole complex WDS could be simplified to that of smaller subsystems formed by either backbone mains or community feeders. The method enables the analysis of all communities independently without physical division of the WDS. It is applied to an optimal-design problem of the C-Town network defined in the literature. The problem is to find the best tradeoff between reducing background leakage and minimizing capital and operational costs. The final solution has a total annual cost of 1.19 million Euro with 54% operational costs and 46% capital costs, respectively. The decomposition process is also consistent with engineering practice on leakage control since one practical strategy is to divide the WDSs into metered zones. Other possible applications of the twin-hierarchy decomposition are further discussed.

This study applies hierarchical decomposition to solve the background leakage control-oriented optimal design problem for the C-Town distribution system. Specifically, the system is first divided into 33 clusters organized in a two level hierarchical structure. The clusters are further classified into trunk clusters and branch clusters respectively. Next, in a sequential manner based on the hierarchy, optimal pressure control strategies for each cluster is addressed with constraints on network performance imposed. As closure of pipes is allowed for leakage reduction, it is found that nearly all clusters are converted into tree networks with pressure control devices inserted.

Awareness of uncertainties is an important issue in every modeling task. In literature, extensive discussion of uncertainties regarding e.g. measurements in general, water consumption, pipe roughness coefficients, etc., can be found. Depending on the modeling aim and the desired outcome (i.e., required accurateness of the results), the question arises what is the impact of not available or poor quality structural data on model simulation results (e.g., if position and properties of pipes and other components are unknown). Therefore, a spanning tree-based algorithm to automatically assemble many possible water distribution models by using free available street network data as input is applied. The obtained uncertainty bandwidth of 180 created models with respect to the pressure head is approximately within ±20m, but can be much lower (±4m) for well performing systems. A qualitative visual comparison of the best performing created system indicates a similar and comparable pressure distribution as in the real system.

Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.

Detection of contamination events in water distribution systems is a crucial task for maintaining water security. Online monitoring is considered as the most cost-effective technology to protect against the impacts of contaminant intrusions. Optimization methods for sensor placement enable automated sensor layout design based on hydraulic and water quality simulation. However, this approach results in an excessive computational burden. In this paper we outline the application of controllability analysis as preprocessing method for sensor placement. Based on case studies we demonstrate that the method decreases the number of decision variables for subsequent optimization dramatically to app. 30 to 40 percent.

This paper aims to explore the impacts of peaking factors on a water distribution system designed for a small city in Germany through model-based analysis. As a case study, the water distribution network was modelled by EPANET and then two specific studies were carried out. The first study tested corresponding system-wide influences on water age and energy consumption if the peaking factors used at design stage are inconsistent with ones in real situation. The second study inspected the possible relationship between the choice of peaking factors and budgets by comparing several different pipe configurations of the distribution system, obtained according to variety of peaking factors. Given the analysis results, the first study reveals that average water age will increase if peaking factors estimated at design stage are larger than real values in that specific system, and vice versa. In contrast, energy consumption will increase if peaking factors defined for system design are smaller than ones in real case, and vice versa. According to the second study, it might be possible to amplify peaking factors for design dramatically by a slight increase in the investment on this system. However, further study on budget estimation with more factors and detailed information considered should be carried out.

Globally, water demand is rising and resources are diminishing. Most of the world's water systems have been highly successful in delivering high-quality water to large populations. However, most of these systems also incur a notable amount of loss in their operations. Water loss from the water supply system has long been a feature of operations management, even in the countries with a well-developed infrastructure and good operating practices. There is no doubt that the sustainable management of water supply system is a challenge for the whole world. Water leakage cannot be completely avoided, but they can be managed so that they remain within economic limits. Some new models of water loss management have been developed recently. As the technical and economic levels varied in different cities, the best practice using in their water supply systems leakage control should be taken suited to local conditions. In this case study a water loss management strategy for a large city in north China was suggested and the technological means for the optimized its managing were provided. The basic principles for structuring district meter areas in this water supply system according its features were developed. The GIS as the tool was used during the study. Based on the field test, the water balance calculations of three DMAs were carried out, and using the fuzzy appraisal method to evaluated their degree of water losses.

© 2014 The Authors. This paper introduces a new approach to water management that is 'Safe & SuRe'. This includes presenting a conceptual framework to link the emerging threats of climate change and variability, rapid urbanization and population growth, energy constraint and tightening environmental regulation through to their consequences on social, economic and environmental recipients. The framework allows identification of the role and need for mitigation, adaptation and coping strategies. The paper proposes definitions and discusses what engineering, organizational and/or social options can potentially develop the degree of resilience and sustainability needed to deal with these 21st century threats. The paper goes on to propose how these approaches might be objectively assessed and identifies gaps in our knowledge that require further research.