Machine Learning-Based Efficient Water Quality Analysis and Prediction

As the most vital natural resource, water covers a vast portion of the Earth’s surface, supporting life and ecosystems. However, urbanization and industrialization have progressed at such an alarming rate that the quality of water has shown a sharp decline, raising an entire spectrum of health and environmental concerns. Traditional methods of assessing water quality often rely on time-consuming and costly laboratory tests and analyses that are not practical for the process of real-time monitoring to mitigate the increasing challenges. This project will utilize machine learning techniques in order to evaluate the overall condition of water quality with the prime aim of determining the potability of the water. The core use of a range of water quality parameters in the essence of methodology forms part of a feature vector for more holistic global assessment of sources of water. These are all intricately woven into the fabric of water quality including pH, hardness, solids, trihalomethanes, sulphate, conductance, organic carbon, and turbidity.