Water Quality Monitoring Model Using Machine Learning

Abstract

Water is one of the vital elements for the existence of life. Quality and accessibility of potable water are growing concerns all over the world. Water from natural sources is usually contaminated with various substances like pathogenic microorganisms, organic waste, fertilizers, sediments, and petroleum that pose health concerns. Taking these factors into consideration, we have designed a system that classifies water as clean or turbid, considering its physical properties. The system makes use of IoT and Machine Learning Technology. It consists of physical and chemical sensors that detect pH, turbidity and TDS to investigate the influencing parameters. The data is collected by sensors converted into a csv file and then submitted to the machine learning model for analysis. When any parameters fall below or above the standard values, the water is classified as turbid. Some pre-existing similar systems use sensors water quality. However, the novelty in our proposed model lies in the use of IoT devices and a ML model that will predict the best result. Also, we will be using advanced sensors that can give precise inputs to the ML model. This tracks the water quality and notifies the user about the water sample being examined.