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

Forecasting domestic water consumption from smart meter readings using statistical methods and artificial neural networks

PublishedThis paper presents an artificial neural network-based model of domestic water consumption. The model is based on real-world data collected from smart meters, and represents a step toward being able to model real-time smart meter data. A range of input schemas are examined, including real meter readings and summary statistics derived from readings, and it is found that the models can predict some consumption but struggle to accurately match in cases of peak usage

Understanding district metered area level leakage using explainable machine learning

This is the final version. Available on open access from IOP Publishing via the DOI in this record14th International Conference on Hydroinformatics, 4 - 8 July 2022, Bucharest, RomaniaUnderstanding the various interrelated effects that result in leakage is vital to the effort to reduce it. This paper aims to understand, at the district metered area (DMA) level, the relationship between leakage and static characteristics of a DMA, i.e. without considering pressure or flow. The characteristics used include the number of pipes and connections, total DMA volume and network density, as well as pipe diameter, length, age, and material statistics. Leakage, especially background and unreported leakage, can be difficult to accurately quantify. Here, the Average Weekly Minimum Night Flow (AWM) over the last 5 years is used as a proxy for leakage. While this may include some legitimate demand, it is generally assumed that minimum night flow, strongly correlates with leakage. A data-driven case study on over 800 real DMAs from UK networks is conducted. Two regression models, a decision tree model and an elastic net linear regression model, are created to predict the AWM of unseen DMAs. Reasonable accuracy was achieved, considering pressure is not an included feature, and the models are investigated for the most prominent features related to leakage.South West Water (SWW

Intermittent water supply systems and their resilience to COVID-19: IWA IWS SG survey

Data availability statement: All relevant data are included in the paper or its Supplementary Information.This is the final version. Available on open access from IWA Publishing via the DOI in this recordThere is limited information about the current state of intermittent water supply (IWS) systems at the global level. A survey was carried out by the Intermittent Water Supply Specialist Group of the International Water Association (IWA IWS SG) to better understand the current state of these systems and challenges that water companies may have faced under COVID-19 pandemic and to capture successful management strategies applied by water utilities. The survey consisted of three parts: (1) general information about IWS systems, (2) current state of IWS and (3) resilience of IWS under COVID-19 conditions, as well as some questions about potential interventions in order to improve system performance in general and under future uncertain conditions. The survey responses were evaluated based on the Safe & SuRe resilience framework, assessing measures of mitigation, adaptation, coping and learning, and exploring organisational and operational responses of IWS utilities. Infrastructure capacity and water resources availability were identified as the main causes of intermittency in most water distribution systems, while intermittent electricity was considered as the main external cause. Participants indicated that some risk assessment process was in place; however, COVID-19 has surpassed any provisions made to address the risks. Lessons learnt highlighted the importance of financial resources, e-infrastructure for efficient system operation and communication with consumers, and the critical role of international knowledge transfer and the sharing of best practice guidelines for improving resilience and transitioning towards continuous water supply.Royal Academy of Engineering (RAE)Engineering and Physical Sciences Research Council (EPSRC

Co-producing research with academics and industry to create a more resilient UK water sector

This is the final version. Available on open access from UCL Press via the DOI in this recordSocietal, economic and environmental impact generated by academic research is a key focus of publicly funded research in the UK. Drawing on experiences from the Safe & SuRe project, a five-year research project that was co-produced with industry, this paper explores the challenges, learnings and benefits of co-producing research with academics and practitioners to create a more resilient UK water sector. Three aspects of the project are explored in detail: the use of a steering group, co-developing research intensively with a water company, and co-dissemination industry-facing events. Emerging themes include: (1) benefits of the industry steering group to develop working relationships and trust among the group; (2) increased dialogue and sharing of information between industry and academics going beyond the one-way communication more commonly reported by STEM academics; and (3) the value of co-disseminating research to maintain and engage new connections and spark new research questions.Engineering and Physical Sciences Research Council (EPSRC

Delivering a multi-functional and resilient urban forest

Tree planting is widely advocated and applied in urban areas, with large-scale projects underway in cities globally. Numerous potential benefits are used to justify these planting campaigns. However, reports of poor tree survival raise questions about the ability of such projects to deliver on their promises over the long-term. Each potential benefit requires different supporting conditions—relating not only to the type and placement of the tree, but also to the broader urban system within which it is embedded. This set of supporting conditions may not always be mutually compatible and may not persist for the lifetime of the tree. Here, we demonstrate a systems-based approach that makes these dependencies, synergies, and tensions more explicit, allowing them to be used to test the decadal-scale resilience of urban street trees. Our analysis highlights social, environmental, and economic assumptions that are implicit within planting projects; notably that high levels of maintenance and public support for urban street trees will persist throughout their natural lifespan, and that the surrounding built form will remain largely unchanged. Whilst the vulnerability of each benefit may be highly context specific, we identify approaches that address some typical weaknesses, making a functional, resilient, urban forest more attainable

Informational entropy : a failure tolerance and reliability surrogate for water distribution networks

Evolutionary algorithms are used widely in optimization studies on water distribution networks. The optimization algorithms use simulation models that analyse the networks under various operating conditions. The solution process typically involves cost minimization along with reliability constraints that ensure reasonably satisfactory performance under abnormal operating conditions also. Flow entropy has been employed previously as a surrogate reliability measure. While a body of work exists for a single operating condition under steady state conditions, the effectiveness of flow entropy for systems with multiple operating conditions has received very little attention. This paper describes a multi-objective genetic algorithm that maximizes the flow entropy under multiple operating conditions for any given network. The new methodology proposed is consistent with the maximum entropy formalism that requires active consideration of all the relevant information. Furthermore, an alternative but equivalent flow entropy model that emphasizes the relative uniformity of the nodal demands is described. The flow entropy of water distribution networks under multiple operating conditions is discussed with reference to the joint entropy of multiple probability spaces, which provides the theoretical foundation for the optimization methodology proposed. Besides the rationale, results are included that show that the most robust or failure-tolerant solutions are achieved by maximizing the sum of the entropies