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dc.contributor.authorRahim, Md Shamsur
dc.contributor.authorKhoi, Anh Nguyen
dc.contributor.authorStewart, Rodney Anthony
dc.contributor.authorGiurco, Damien
dc.contributor.authorBlumenstein, Michael
dc.date.accessioned2020-07-02T01:51:18Z
dc.date.available2020-07-02T01:51:18Z
dc.date.issued2020
dc.identifier.issn2073-4441
dc.identifier.doi10.3390/w12010294
dc.identifier.urihttp://hdl.handle.net/10072/395083
dc.description.abstractDigital 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.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofpagefrom294:1
dc.relation.ispartofpageto294:26
dc.relation.ispartofissue1
dc.relation.ispartofjournalWater
dc.relation.ispartofvolume12
dc.subject.fieldofresearchEnvironmental sciences
dc.subject.fieldofresearchcode41
dc.subject.keywordsScience & Technology
dc.subject.keywordsPhysical Sciences
dc.subject.keywordsWater Resources
dc.subject.keywordsdata analytics
dc.subject.keywordsdigital metering data
dc.titleMachine Learning and Data Analytic Techniques in Digital Water Metering: A Review
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationRahim, MS; Khoi, AN; Stewart, RA; Giurco, D; Blumenstein, M, Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review, Water, 2020, 12 (1), pp. 294:1-294:26
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-07-02T01:47:21Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 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.
gro.hasfulltextFull Text
gro.griffith.authorStewart, Rodney A.
gro.griffith.authorNguyen, Khoi A.


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