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- W3000514460 endingPage "294" @default.
- W3000514460 startingPage "294" @default.
- W3000514460 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 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 advanced ML and DA techniques, 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." @default.
- W3000514460 created "2020-01-23" @default.
- W3000514460 creator A5030294949 @default.
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- W3000514460 creator A5037109927 @default.
- W3000514460 creator A5045600512 @default.
- W3000514460 creator A5074465966 @default.
- W3000514460 date "2020-01-19" @default.
- W3000514460 modified "2023-10-06" @default.
- W3000514460 title "Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review" @default.
- W3000514460 cites W1049614015 @default.
- W3000514460 cites W1165451547 @default.
- W3000514460 cites W1262302848 @default.
- W3000514460 cites W1541087966 @default.
- W3000514460 cites W1674363111 @default.
- W3000514460 cites W1812222446 @default.
- W3000514460 cites W1966978090 @default.
- W3000514460 cites W1969832861 @default.
- W3000514460 cites W1974853958 @default.
- W3000514460 cites W1988313052 @default.
- W3000514460 cites W1989237617 @default.
- W3000514460 cites W1992079046 @default.
- W3000514460 cites W1992167377 @default.
- W3000514460 cites W1995719499 @default.
- W3000514460 cites W1996510517 @default.
- W3000514460 cites W2010600455 @default.
- W3000514460 cites W2012820235 @default.
- W3000514460 cites W2015027435 @default.
- W3000514460 cites W2015399489 @default.
- W3000514460 cites W2023045142 @default.
- W3000514460 cites W2033769356 @default.
- W3000514460 cites W2034621243 @default.
- W3000514460 cites W2036628004 @default.
- W3000514460 cites W2037821918 @default.
- W3000514460 cites W2037910312 @default.
- W3000514460 cites W2039449057 @default.
- W3000514460 cites W2043500332 @default.
- W3000514460 cites W2044317054 @default.
- W3000514460 cites W2045044116 @default.
- W3000514460 cites W2057136602 @default.
- W3000514460 cites W2060154895 @default.
- W3000514460 cites W2062152884 @default.
- W3000514460 cites W2062981820 @default.
- W3000514460 cites W2063316777 @default.
- W3000514460 cites W2064675550 @default.
- W3000514460 cites W2066241904 @default.
- W3000514460 cites W2066657001 @default.
- W3000514460 cites W2074895072 @default.
- W3000514460 cites W2076996835 @default.
- W3000514460 cites W2077147343 @default.
- W3000514460 cites W2084809223 @default.
- W3000514460 cites W2094055282 @default.
- W3000514460 cites W2111039503 @default.
- W3000514460 cites W2118364057 @default.
- W3000514460 cites W2122646361 @default.
- W3000514460 cites W2126626732 @default.
- W3000514460 cites W2145339207 @default.
- W3000514460 cites W2146576294 @default.
- W3000514460 cites W2159826290 @default.
- W3000514460 cites W2160815625 @default.
- W3000514460 cites W2167611291 @default.
- W3000514460 cites W2169055640 @default.
- W3000514460 cites W2175229599 @default.
- W3000514460 cites W2259001943 @default.
- W3000514460 cites W2277444012 @default.
- W3000514460 cites W2279630689 @default.
- W3000514460 cites W2341865734 @default.
- W3000514460 cites W2343167814 @default.
- W3000514460 cites W2395254444 @default.
- W3000514460 cites W2406388864 @default.
- W3000514460 cites W2412901959 @default.
- W3000514460 cites W2470765406 @default.
- W3000514460 cites W2491745777 @default.
- W3000514460 cites W2553785657 @default.
- W3000514460 cites W2559352034 @default.
- W3000514460 cites W2561470123 @default.
- W3000514460 cites W2567091547 @default.
- W3000514460 cites W2586439557 @default.
- W3000514460 cites W2587497793 @default.
- W3000514460 cites W2599404478 @default.
- W3000514460 cites W2600845876 @default.
- W3000514460 cites W2624139643 @default.
- W3000514460 cites W2750962140 @default.
- W3000514460 cites W2765676269 @default.
- W3000514460 cites W2767692305 @default.
- W3000514460 cites W2780842280 @default.
- W3000514460 cites W2783269460 @default.
- W3000514460 cites W2783288398 @default.
- W3000514460 cites W2789315551 @default.
- W3000514460 cites W2793750707 @default.
- W3000514460 cites W2794150200 @default.
- W3000514460 cites W2794188809 @default.
- W3000514460 cites W2795201804 @default.
- W3000514460 cites W2797478162 @default.
- W3000514460 cites W2800612837 @default.
- W3000514460 cites W2804875398 @default.
- W3000514460 cites W2805018168 @default.
- W3000514460 cites W2808860092 @default.