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  • Re-engineering traditional urban water management practices with smart metering and informatics

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    NguyenPUB5082.pdf (1.130Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Nguyen, Khoi A
    Stewart, Rodney A
    Zhang, Hong
    Sahin, Oz
    Siriwardene, Nilmini
    Griffith University Author(s)
    Stewart, Rodney A.
    Zhang, Hong
    Sahin, Oz
    Year published
    2018
    Metadata
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    Abstract
    Current practice for the design of an urban water system usually relies on various models that are often founded on a number of assumptions on how bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters and advanced data analytics allow for a new era of using the continuous ‘big data’ generated by these meter fleets to create an intelligent system for urban water management to overcome this problem. The aim of this research ...
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    Current practice for the design of an urban water system usually relies on various models that are often founded on a number of assumptions on how bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters and advanced data analytics allow for a new era of using the continuous ‘big data’ generated by these meter fleets to create an intelligent system for urban water management to overcome this problem. The aim of this research is to provide infrastructure planners with a detailed understanding of how granular data generated by an intelligent water management system (Autoflow©) can be utilised to obtain significant efficiencies throughout different stages of an urban water cycle, from supply, distribution, customer engagement, and even wastewater treatment.
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    Journal Title
    Environmental Modelling & Software
    Volume
    101
    DOI
    https://doi.org/10.1016/j.envsoft.2017.12.015
    Copyright Statement
    © 2018 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Water resources engineering
    Publication URI
    http://hdl.handle.net/10072/376326
    Collection
    • Journal articles

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