A hybrid machine learning and embedded IoT-based water quality monitoring system

Since access to clean and safe water is one of life's most basic needs, the exponential growth of the world's population makes it vital to guarantee a workable framework. Manually collecting samples and sending them to a research center for discovery and analysis is the typical approach for gathering information on water qualities. However, this approach is illogical in the long run because it requires a lot of 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 classifier and reliability matrices. This study considers water's physical and chemical parameters to evaluate the level of water pollutants present 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 to predict 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.