Design and Development of a Smart Water Quality Monitoring System for Real-Time Water Assessment

Because access to clean, safe water is one of life’s most basic needs, the world’s exponential population growth makes it vital to ensure a workable framework. Manually collecting samples and sending them to a research center for analysis is the typical approach to gathering water-quality information. However, this approach is impractical in the long run because it requires significant time and labor. This paper aimed to construct, test, and evaluate the usefulness of machine learning and the Internet of Things (IoT) at water storage stations. First, we developed a system prototype and assessed its performance using classification and reliability matrices. This study considers water’s physical and chemical parameters to assess pollutant levels in drinking water. The parameters measured include temperature, pH, turbidity, Dissolved Oxygen (DO), Total Dissolved Solids (TDS), Oxidation-Reduction Potential (ORP), and electrical conductivity. After analyzing the sensor data, we used Artificial Neural Network (ANN) and Support Vector Machine (SVM) machine learning algorithms to forecast the impurity level of the water measured. The performance showed that the ANN models used have the highest accuracy and are the most suitable for predicting water source and status. We also introduced a water treatment method to provide an automated corrective measure based on a specific amount of water contamination. Based on the system’s results, we concluded that AI and IoT are more efficient in remotely monitoring safe and harmful water conditions.