Smart Urban Water Quality Prediction System Using Machine Learning

Abstract

The World Health Organization says that every year more than 3.4 million people die as a result of water-related diseases. Quality of water serves as a powerful environmental determinant and a foundation for the prevention and control of waterborne diseases. The project aims to design a water quality prediction system using Machine learning based on the water standards suggested by BIS to prevent deaths due to water related diseases. The quality is predicted based on parameters such as pH, Temperature, TDS, Turbidity and Conductivity value. The dataset is preprocessed and split into test and training data. The data is fed into regression algorithm and been evaluated. Sensors that can measure the water parameters are also been implemented. A webpage interfaced with the Machine Learning model is created to upload sensor values and the corresponding water quality is predicted. This project can be used in urban areas to predict the quality of the drinking water thereby preventing the spread of diseases such as dysentery, typhoid and cholera due to consumption of contaminated water.