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  • Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review

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    Author(s)
    Rahim, Md Shamsur
    Khoi, Anh Nguyen
    Stewart, Rodney Anthony
    Giurco, Damien
    Blumenstein, Michael
    Griffith University Author(s)
    Stewart, Rodney A.
    Nguyen, Khoi A.
    Year published
    2020
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    Abstract
    Digital or intelligent water meters are being rolled out globally as a crucial component in improving urban water management. This is because of their ability to frequently send water consumption information electronically and later utilise the information to generate insights or provide feedback to consumers. Recent advances in machine learning (ML) and data analytic (DA) technologies have provided the opportunity to more effectively utilise the vast amount of data generated by these meters. Several studies have been conducted to promote water conservation by analysing the data generated by digital meters and providing ...
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    Digital or intelligent water meters are being rolled out globally as a crucial component in improving urban water management. This is because of their ability to frequently send water consumption information electronically and later utilise the information to generate insights or provide feedback to consumers. Recent advances in machine learning (ML) and data analytic (DA) technologies have provided the opportunity to more effectively utilise the vast amount of data generated by these meters. Several studies have been conducted to promote water conservation by analysing the data generated by digital meters and providing feedback to consumers and water utilities. The purpose of this review was to inform scholars and practitioners about the contributions and limitations of ML and DA techniques by critically analysing the relevant literature. We categorised studies into five main themes: (1) water demand forecasting; (2) socioeconomic analysis; (3) behaviour analysis; (4) water event categorisation; and (5) water-use feedback. The review identified significant research gaps in terms of the adoption of advancedMLandDAtechniques, which could potentially lead to water savings and more efficient demand management. We concluded that further investigations are required into highly personalised feedback systems, such as recommender systems, to promote water-conscious behaviour. In addition, advanced data management solutions, effective user profiles, and the clustering of consumers based on their profiles require more attention to promote water-conscious behaviours.
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    Journal Title
    Water
    Volume
    12
    Issue
    1
    DOI
    https://doi.org/10.3390/w12010294
    Copyright Statement
    © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Environmental sciences
    Science & Technology
    Physical Sciences
    Water Resources
    data analytics
    digital metering data
    Publication URI
    http://hdl.handle.net/10072/395083
    Collection
    • Journal articles

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