Machine learning and data analytic techniques in digitalwater metering: A review

Rahim, MS; Nguyen, KA; Stewart, RA; Giurco, D; Blumenstein, M

Machine learning and data analytic techniques in digitalwater metering: A review

Rahim, MS Nguyen, KA Stewart, RA Giurco, D

Blumenstein, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Water (Switzerland), 2020, 12, (1)
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Rahim, MS
dc.contributor.author	Nguyen, KA
dc.contributor.author	Stewart, RA
dc.contributor.author	Giurco, D https://orcid.org/0000-0002-1707-9531
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2021-02-27T22:53:27Z
dc.date.available	2021-02-27T22:53:27Z
dc.date.issued	2020-01-01
dc.identifier.citation	Water (Switzerland), 2020, 12, (1)
dc.identifier.issn	2073-4441
dc.identifier.issn	2073-4441
dc.identifier.uri	http://hdl.handle.net/10453/146514
dc.description.abstract	© 2020 by the authors. 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.
dc.language	English
dc.publisher	MDPI
dc.relation	http://purl.org/au-research/grants/arc/LP160100215
dc.relation.ispartof	Water (Switzerland)
dc.relation.isbasedon	10.3390/w12010294
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Machine learning and data analytic techniques in digitalwater metering: A review
dc.type	Journal Article
utslib.citation.volume	12
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/Strength - ISF - Institute for Sustainable Futures
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	open_access	*
dc.date.updated	2021-02-27T22:53:24Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	1

Abstract:

© 2020 by the authors. 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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http://hdl.handle.net/10453/146514