An Internet of Things-Based Smart Water Meter with Machine Learning-Aided Water Quality Assessment

This paper presents an Internet of Things (IoT)-based smart water meter with machine learning (ML)-aided water quality assessment capability. A flow rate sensor is used to measure water consumption while pH and turbidity sensors are used for water quality assessment. The collected data is sent to a remote server via a cellular network where it is used for monitoring purposes by both the utility company and customers. The system assesses the collected data against relevant thresholds and provides appropriate notifications to the service provider and the customers. The thresholds for water quality are based on the national standards for potable water while those for consumption are determined by the average monthly water consumption. This paper considers National Water and Sewerage Corporation (NWSC), the largest water utility company in Uganda, as a case study. A total of 1,760 samples collected by NWSC in the Kampala service area in 2022 were assessed using the feature selection algorithm of ML. The most dominant parameters were determined as residual chlorine, pH, turbidity, conductivity, and apparent color. In this paper, only pH and turbidity are considered.