Machine learning-driven intelligent water quality assessment for enhanced drinking safety and real-time consumer awareness

Abstract

As to the sphere of smart water management and managing water Internet of Things (IoT) systems, water condition safety for drinking is very important. The proposed methodology, known as the Smart Water Consumption Monitoring System (SWCMS), is based on the WaterNet data-set acquired from a standard data repository for training the selected machine learning (ML) models. For water quality parameters such as temperature, turbidity, pH, and some chemical concentrations, the system uses real-time sensors. At the testing phase, information received from the sensors is time-stamped, and with the utilization of applicable ML approaches, potential challenges; assessment of water quality is processed. This encompasses the employment of advanced instruments for the detection of water quality with concentration on pH and other chemical values through a detection accuracy rate of over 95% on any other signs of abnormalities. This processed information is further availed with the timestamps to the consumers’ mobile phones through a user interface application for real-time awareness and timely response. With the aid of timely information about their drinking water, the SWCMS increases the water safety parameter by 90% and the overall consumer awareness by 92.5%, thereby creating an effective health parameter among the public.