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

Autor: Md Shamsur Rahim, Khoi Anh Nguyen, Rodney Anthony Stewart, Damien Giurco, Michael Blumenstein
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Water, Vol 12, Iss 1, p 294 (2020)
Druh dokumentu: article
ISSN: 2073-4441
12010294
DOI: 10.3390/w12010294
Popis: 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.
Databáze: Directory of Open Access Journals
Externí odkaz: