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

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.

Perceptions of Teachers in Training on Water Issues and Their Relationship to the SDGs

“…According to our view, it is essential to conduct further research that involves professionals working in education, whether formal, non-formal or informal, especially teachers or social educators, since they are agents of social change in terms of sustainable development who provide the knowledge and awareness that people require to become more pro-environmental [11,33,34]. Furthermore, in the case of teachers within governments where education is compulsory, they are professionals who can reach out to society as a whole during a large part of their lives, which is decisive for the rest of it.…”

“…Therefore, there is a need for society to be aware and act responsibly through pro-environmental behaviours. Several studies highlight the importance of raising awareness among the population, with education being a relevant factor in the demand management and proper use of water [10,23,[32][33][34][35][36][37][38][39]. In fact, it seems that the higher the educational level of people, the greater their awareness about the quality of water and the damages caused by pollution [10], although it has no correlation with conservation behaviours [6].…”

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

“…Some remarkable developments include the remote monitoring of reservoirs and water supply using satellite data-e.g., the Sentinel-1 Program [101], which predicts flood risk and water balance using Earth observation and hydrological data (i.e., rainfall, temperature, etc.) [102], using chemical sensors for real-time water quality monitoring and pollution tracing [103,104], applying advanced machine learning techniques for improving water quality forecasting and urban water management [105,106], etc. The water quality sector was examined by [107] and the water treatment industry by [108].…”

Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control

“…Examples for short-term demand forecasting can be found in [6,7], which consider the daily demand prediction for a single person. While the work in [8] gives a broader review on machine learning and data analytic techniques for urban water management.…”