Real Time Monitoring of Water Quality for Rural Areas: A Machine Learning and Internet of Things Approach

A water quality monitoring system can aid in preserving the environment, ensuring the security of nearby water sources, and fostering economic growth in rural areas. This results in the development of a system, employing the Internet of Things (IoT) and Machine Learning to monitor the quality of water. This paper discusses the characteristics of water that explains whether it is fit for human consumption or not. The pH and Turbidity sensors are dipped in water samples acquired from wells, lakes, rivers, ponds, and other places are used to inform the development of an effective model. The data will be delivered from the sensors to the IDE, where it will then be sent to the cloud server. The model effectively accounts for test tables, where 1 indicates the water is fit for drinking and 0 indicates the water is unfit. The model is validated by employing Support Vector Machine, Random Forest and XG Boost methods. The maximum accuracy of 95.12% was observed using XGBoost. We have successfully implemented the real-time data for the water quality monitoring system for rural areas using Machine Learning and IoT which lets us know whether the water is fit for drinking or not.