An all-purpose method for optimal pressure sensor placement in water distribution networks based on graph signal analysis.

Many researchers have addressed the challenge of optimal pressure sensor placement for different purposes, such as leakage detection, model calibration, state estimation, etc. However, pressure data often need to serve multiple purposes, and a method to optimize sensor locations with versatility for various objectives is still lacking. In this paper, a graph-based optimal sensor placement (GOSP) framework is proposed, which aims to provide a robust and all-purpose approach to identify critical points for pressure monitoring. By analysing the spatial variation frequencies of WDN pressures, the relationship between measurements and the global variation of original pressures is established. On this basis, the D-optimality criterion is adopted to formulate the objective of GOSP, which aims to maximize the information on the spatial distribution of pressures that can be obtained from measurements. The new-proposed objective ensures that the sensor locations are compatible with various application scenarios. The proposed method was applied to a real-life distribution network, and was compared with other optimal sensor placement methods oriented towards burst detection and pipe roughness calibration. Based on comparative studies in different scenarios including unknown pressure estimation, burst detection, and model calibration, the effectiveness and robustness of the proposed method have been proved.

Customised-sampling approach for pipe failure prediction in water distribution networks.

This paper presents a new methodology for addressing imbalanced class data for failure prediction in Water Distribution Networks (WDNs). The proposed methodology relies on existing approaches including under-sampling, over-sampling, and class weighting as primary strategies. These techniques aim to treat the imbalanced datasets by adjusting the representation of minority and majority classes. Under-sampling reduces data in the majority class, over-sampling adds data to the minority class, and class weighting assigns unequal weights based on class counts to balance the influence of each class during machine learning (ML) model training. In this paper, the mentioned approaches were used at levels other than "balance point" to construct pipe failure prediction models for a WDN with highly imbalanced data. F1-score, and AUC-ROC, were selected to evaluate model performance. Results revealed that under-sampling above the balance point yields the highest F1-score, while over-sampling below the balance point achieves optimal results. Employing class weights during training and prediction emphasises the efficacy of lower weights than the balance. Combining under-sampling and over-sampling to the same ratio for both majority and minority classes showed limited improvement. However, a more effective predictive model emerged when over-sampling the minority class and under-sampling the majority class to different ratios, followed by applying class weights to balance data.

A review of limitations and potentials of desalination as a sustainable source of water.

For centuries, desalination, in one way or another, has helped alleviate water scarcity. Over time, desalination has gone through an evolutionary process influenced largely by available contemporary technology. This improvement, for the most part, was reflected in the energy efficiency and, in turn, in terms of the cost-effectiveness of this practice. Thanks to such advancements, by the 1960s, the desalination industry experienced notable exponential growth, becoming a formidable option to supplement conventional water resources with a reliable non-conventional resource. That said, often, there are pressing associated issues, most notably environmental, socioeconomic, health, and relatively recently, agronomic concerns. Such reservations raise the question of whether desalination is indeed a sustainable solution to current water supply problems. This is exceptionally important to understand in light of the looming water and food crises. This paper, thus, tends to review these potential issues from the sustainability perspective. It is concluded that the aforementioned issues are indeed major concerns, but they can be mitigated by actions that consider the local context. These may be either prophylactic, proactive measures that require careful planning to tailor the situation to best fit a given region or reactive measures such as incorporating pre- (e.g., removing particles, debris, microorganisms, suspended solids, and silt from the intake water prior to the desalination process) and post-treatments (e.g., reintroducing calcium and magnesium ions to water to enhance its quality for irrigation purposes) to target specific shortcomings of desalination.

Leak detection and localization in water distribution networks using conditional deep convolutional generative adversarial networks.

This paper explores the use of 'conditional convolutional generative adversarial networks' (CDCGAN) for image-based leak detection and localization (LD&L) in water distribution networks (WDNs). The method employs pressure measurements and is based on four pillars: (1) hydraulic model-based generation of leak-free training data by taking into account the demand uncertainty, (2) conversion of hydraulic model input demand-output pressure pairs into images using kriging interpolation, (3) training of a CDCGAN model for image-to-image translation, and (4) using the structural similarity (SSIM) index for LD&L. SSIM, computed over the entire pressure distribution image is used for leak detection, and a local estimate of SSIM is employed for leak localization. The CDCGAN model employed in this paper is based on the pix2pix architecture. The effectiveness of the proposed methodology is demonstrated on leakage datasets under various scenarios. Results show that the method has an accuracy of approximately 70% for real-time leak detection. The proposed method is well-suited for real-time applications due to the low computational cost of CDCGAN predictions compared to WDN hydraulic models, is robust in presence of uncertainty due to the nature of generative adversarial networks, and scales well to large and variable-sized monitoring data due to the use of an image-based approach.

Multi-variable approach to groundwater vulnerability elucidation: A risk-based multi-objective optimization model.

Groundwater vulnerability mapping is essential in environmental management since there is an increase in contamination caused by excessive population growth. However, to our knowledge, there is rare research dedicated to optimizing the groundwater vulnerability models, considering risk conditions, using a robust multi-objective optimization algorithm coupled with a multi-criteria decision-making model (MCDM). This study filled this knowledge gap by developing an innovative hybrid risk-based multi-objective optimization model using three distinguished models. The first model generated two series of scenarios for rate modifications associated with two common contaminations, Nitrate and Sulfate, based on susceptibility index (SI) and DRASTICA models. The second model was a multi-objective optimization framework using non-dominated sorting genetic algorithms- II and III (NSGA-II and NSGA-III), considering uncertainties in the input rates by the conditional value-at-risk (CVaR) technique. Finally, the third model was a well-known MCDM model, the COmplex PRoportional ASsessment (COPRAS), which identified the best compromise solution among Pareto-optimal solutions for weights of the contaminations. Regarding the Sulfate's results, although the optimized DRASTICA model led to the same correlation as the initial model, 0.7, the optimized SI model increased the correlation to 0.8 compared to the initial model as 0.58. For the Nitrate, both the optimized SI and the optimized DRASTICA models raised the correlation to 0.6 and 0.7 compared to the initial model with a correlation value of 0.36, respectively. Hence, the best and the lowest correlation among the optimized models were between SI and Sulfate concentration and SI and Nitrate concentration, respectively.

Moving to a future of smart stormwater management: A review and framework for terminology, research, and future perspectives.

Stormwater hazards are a significant threat across the globe. These are continuing to increase in line with urbanisation and climate change, leading to a recognition that the historic paradigm of passive management using centralised infrastructure is insufficient to manage future hazards to our society, environment, and economy. The cross-sector Internet of Things revolution has inspired a new generation of smart stormwater management systems which offer an effective, cost beneficial and adaptive solution to enhance network capacities and reduce hazards. However, despite growing prominence within research, this technology remains under-utilised, in a large part due to fragmented and inconsistent alignment and terminology, obscuring the strategic co-ordination of research. We respond to this through systematically reviewing the terminology, practice and trajectory for smart stormwater management and developing a framework which can be applied to both coordinate and understand the existing research landscape, as well as identifying key research gaps for future development. We find that literature almost universally agrees that smart technology is, or will be, beneficial to stormwater management and that technology has reached partial maturity in terms of quantity management, although this has not yet transferred to water quality. However, research is dominated by proof-of-concept modelling studies, with limited practical application beyond real time control of large assets, individual pilot studies and monitoring. We recommend that future research explores and evidences the substantial benefits likely through expanding current implementation towards a coordinated, decentralised, and optimised catchment-scale approach.

General resilience: Conceptual formulation and quantitative assessment for intervention development in the urban wastewater system.

General resilience addresses the resilience of a water system to any threat including unknowns, in contrast to specified resilience to individual identified threats. However, quantification of general resilience is challenging and previous assessments have typically been qualitative or based on system properties that are assumed to be indicative of resilient performance. Here we present a General Resilience Assessment Methodology (GRAM), which uses a middle-state based approach to decompose general resilience into contributing components to provide a quantitative and performance-based resilience assessment. GRAM enables the accounting of the effects of any threat if all modes of system failure are identifiable. It is applied to an integrated urban wastewater system where five interventions are explored. The results obtained show that whilst substantial improvements in specified resilience are achieved, increasing the general resilience of the system is challenging. However, general resilience analysis enables identification of system failure modes to which level of service is least resilient and highlights key opportunities for intervention development. GRAM is beneficial as it can inform the development of interventions to increase the resilience of a system to unknowns such as unforeseeable natural hazards in a quantifiable manner.

COVID-19 and the UK water sector: Exploring organizational responses through a resilience framework.

The unprecedented scale and impact of the COVID-19 pandemic have required organizations to adapt all facets of their operations. The impact on the UK water sector extends beyond engineering and treatment processes, with social, economic and environmental consequences. Semi-structured interviews were conducted with executives from 10 UK water companies to investigate the organizational response to the pandemic, and how their response impacted operational delivery. The Safe and SuRe framework was used to structure interview questions and analysis. Emergent themes of changes to customer behaviour, changes to operational practices and industry collaboration were mapped onto the framework and a ripple effect map developed. Lessons learnt highlight a failure to adequately prepare for the scale of the threat, the success of sector-level collaboration and a need to embrace new ways of working.

Modular interdependency analysis for water distribution systems.

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.

COVID-19 and the water sector: understanding impact, preparedness and resilience in the UK through a sector-wide survey.

The COVID-19 pandemic led to drastically altered working practices. During the UK lockdown, a questionnaire was distributed to water professionals to understand their experiences and perceptions of organisational response. Findings were evaluated on the measures of mitigation, adaptation, coping and learning. Employees' perceived there were adequate procedures to mitigate a threat, partly due to preparations for Brexit. Participants quickly adapted, with eighty-four percent working from home. Coping was experienced at an individual and sector level. IT issues and care responsibilities made it harder for individuals to cope, but good communication and signposting of support helped. Eighty percent felt able to continue their usual role, implying coping mechanisms were effective. At the sector level, coping involved the ability to meet an increased water demand with a remote workforce. Lessons learned highlight the importance of communication and collaboration. Future crisis plans should prepare for prolonged crises of international magnitude and multiple threats.

Strategic planning of the integrated urban wastewater system using adaptation pathways.

Emerging threats such as climate change and urbanisation pose an unprecedented challenge to the integrated management of urban wastewater systems, which are expected to function in a reliable, resilient and sustainable manner regardless of future conditions. Traditional long term planning is rather limited in developing no-regret strategies that avoid maladaptive lock-ins in the near term and allow for flexibility in the long term. In this study, a novel adaptation pathways approach for urban wastewater management is developed in order to explore the compliance and adaptability potential of intervention strategies in a long term operational period, accounting for different future scenarios and multiple performance objectives in terms of reliability, resilience and sustainability. This multi-criteria multi-scenario approach implements a regret-based method to assess the relative performance of two types of adaptation strategies: (I) standalone strategies (i.e. green or grey strategies only); and (II) hybrid strategies (i.e. combined green and grey strategies). A number of adaptation thresholds (i.e. the points at which the current strategy can no longer meet defined objectives) are defined to identify compliant domains (i.e. periods of time in a future scenario when the performance of a strategy can meet the targets). The results obtained from a case study illustrate the trade-off between adapting to short term pressures and addressing long term challenges. Green strategies show the highest performance in simultaneously meeting near and long term needs, while grey strategies are found less adaptable to changing circumstances. In contrast, hybrid strategies are effective in delivering both short term compliance and long term adaptability. It is also shown that the proposed adaption pathways method can contribute to the identification of adaptation strategies that are developed as future conditions unfold, allowing for more flexibility and avoiding long term commitment to strategies that may cause maladaptation. This provides insights into the near term and long term planning of ensuring the reliability, resilience and sustainability of integrated urban drainage systems.

Modelling the future impacts of urban spatial planning on the viability of alternative water supply.

Greywater recycling and rainwater harvesting have the potential to increase the resilience of water management and reduce the need for investment in conventional water supply schemes. However, their water-savings would partly depend on the location and built-form of urban development and hence its household sizes and rainwater per dwelling. We have therefore tested how spatial planning options would affect the future viability of alternative water supply in the Greater South East of England. Our integrated modelling framework, for the first time, forecasts the future densities and variability of built-form to provide inputs to the modelling of alternative water supply. We show that using projections of the existing housing stock would have been unsound, and that using standard dwelling types and household sizes would have substantially overestimated the water-savings, by not fully representing how the variability in dwelling dimensions and household-sizes would affect the cost effectiveness of these systems. We compare the spatial planning trend over a 30 year period with either compaction at higher densities within existing urban boundaries, or market-led more dispersed development. We show how the viability of alternative water supply would differ between these three spatial planning options. The water-savings of rainwater harvesting would vary greatly at a regional scale depending on residential densities and rainfall. Greywater recycling would be less affected by spatial planning but would have a finer balance between system costs and water-savings and its feasibility would vary locally depending on household sizes and water efficiency. The sensitivity of the water savings to differences in rainfall and water prices would vary with residential density. The findings suggest that forecasts of residential densities, rainfall and the water price could be used in conjunction with more detailed local studies to indicate how spatial planning would affect the future water saving potential of alternative water supply.

Exploring wastewater system performance under future threats: Does enhancing resilience increase sustainability?

Sustainability and resilience are both key considerations in the design and operation of wastewater systems. However, there is currently a lack of understanding of the relationship between these two goals and of the effects of increasing resilience on sustainability. This paper, therefore, presents a framework for analysis of the effects of resilience-enhancing interventions on sustainability, and applies this to an urban wastewater system. Given that sustainability addresses the long term, the framework includes a novel sustainability assessment approach which captures a continuum of potential future conditions and enables identification of tipping points where applicable. This method allows a wide range of potential futures to be captured whilst removing the need to develop scenarios or future projections. While it may be possible to develop interventions that are beneficial in terms of their effects on both resilience and sustainability, the results obtained from the case study demonstrate that implementing measures designed to increase resilience of an integrated urban wastewater system does not guarantee a universal improvement in sustainability. Therefore, when proposing measures to increase resilience, the potential effects on sustainability should be considered also. It is also shown that the extent of any negative effects on system sustainability can vary significantly depending on future conditions, with the case study intervention (increasing pump capacity) achieving the highest degree of sustainability if rainfall depths or imperviousness in the catchments reduce. However, trade-offs between sustainability indicators are present irrespective of future conditions. Furthermore, while an intervention that enhances resilience may be considered sustainable with respect to specific indicators under current conditions, tipping points exist and it will cease to be sustainable if future threat magnitudes exceed these.

Topological attributes of network resilience: A study in water distribution systems.

Resilience has been increasingly pursued in the management of water distribution systems (WDSs) such that a system can adapt to and rapidly recover from potential failures in face of a deep uncertain and unpredictable future. Topology has been assumed to have a great impact on resilience of WDSs, and is the basis of many studies on assessing and building resilience. However, this fundamental assumption has not been justified and requires investigation. To address this, a novel framework for mapping between resilience performance and network topological attributes is proposed. It is applied to WDSs here but can be adaptable to other network systems. In the framework, resilience is comprehensively assessed using stress-strain tests which measure system performance on six metrics corresponding to system resistance, absorption and restoration capacities. Six key topological attributes of WDSs (connectivity, efficiency, centrality, diversity, robustness and modularity) are studied by mathematical abstraction of WDSs as graphs and measured by eight statistical metrics in graph theory. The interplay between resilience and topological attributes is revealed by the correlations between their corresponding metrics, based on 85 WDSs with different sizes and topological features. Further, network variants from a single WDS are generated to uncover the value of topological attribute metrics in guiding the extension/rehabilitation design of WDSs towards resilience. Results show that only certain aspects of resilience performance, i.e. spatial and temporal scales of failure impacts, are strongly influenced by some (not all) topological attributes, i.e. network connectivity, efficiency, modularity and centrality. Metrics for describing the topological attributes of WDSs need to be carefully selected; for example, clustering coefficient is found to be weakly correlated with resilience performance compared to other metrics of network connectivity (due to the grid-like structures of WDSs). Topological attribute metrics alone are not sufficient to guide the design of resilient WDSs and key details such as the location of water sources also need to be considered.

Attribute-based intervention development for increasing resilience of urban drainage systems.

Resilience building commonly focuses on attributes such as redundancy. Whilst this may be effective in some cases, provision of specific attributes does not guarantee resilient performance and research is required to determine the suitability of such approaches. This study uses 250 combined sewer system virtual case studies to explore the effects of two attribute-based interventions (increasing distributed storage and reducing imperviousness) on performance-based resilience measures. These are found to provide improvement in performance under system failure in the majority of case studies, but it is also shown that attribute-based intervention development can result in reduced resilience.

A framework to support decision making in the selection of sustainable drainage system design alternatives.

This paper presents a new framework for decision making in sustainable drainage system (SuDS) scheme design. It integrates resilience, hydraulic performance, pollution control, rainwater usage, energy analysis, greenhouse gas (GHG) emissions and costs, and has 12 indicators. The multi-criteria analysis methods of entropy weight and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were selected to support SuDS scheme selection. The effectiveness of the framework is demonstrated with a SuDS case in China. Indicators used include flood volume, flood duration, a hydraulic performance indicator, cost and resilience. Resilience is an important design consideration, and it supports scheme selection in the case study. The proposed framework will help a decision maker to choose an appropriate design scheme for implementation without subjectivity.

Reliable, resilient and sustainable water management: the Safe & SuRe approach.

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.

Global resilience analysis of water distribution systems.

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 also reveals that increased resilience to one failure mode may decrease resilience to another and increasing system capacity may delay the system's recovery in some situations. It is also shown that selecting an appropriate level of detail for hydraulic models is of great importance in resilience analysis. The method can be used as a comprehensive diagnostic framework to evaluate a range of interventions for improving system resilience in future studies.

A global analysis approach for investigating structural resilience in urban drainage systems.

Building resilience in urban drainage systems requires consideration of a wide range of threats that contribute to urban flooding. Existing hydraulic reliability based approaches have focused on quantifying functional failure caused by extreme rainfall or increase in dry weather flows that lead to hydraulic overloading of the system. Such approaches however, do not fully explore the full system failure scenario space due to exclusion of crucial threats such as equipment malfunction, pipe collapse and blockage that can also lead to urban flooding. In this research, a new analytical approach based on global resilience analysis is investigated and applied to systematically evaluate the performance of an urban drainage system when subjected to a wide range of structural failure scenarios resulting from random cumulative link failure. Link failure envelopes, which represent the resulting loss of system functionality (impacts) are determined by computing the upper and lower limits of the simulation results for total flood volume (failure magnitude) and average flood duration (failure duration) at each link failure level. A new resilience index that combines the failure magnitude and duration into a single metric is applied to quantify system residual functionality at each considered link failure level. With this approach, resilience has been tested and characterised for an existing urban drainage system in Kampala city, Uganda. In addition, the effectiveness of potential adaptation strategies in enhancing its resilience to cumulative link failure has been tested.

Bayesian networks in environmental and resource management.

This overview article for the special series, "Bayesian Networks in Environmental and Resource Management," reviews 7 case study articles with the aim to compare Bayesian network (BN) applications to different environmental and resource management problems from around the world. The article discusses advances in the last decade in the use of BNs as applied to environmental and resource management. We highlight progress in computational methods, best-practices for model design and model communication. We review several research challenges to the use of BNs in environmental and resource management that we think may find a solution in the near future with further research attention.