An Efficient Automated Water Quality Assessment System utilising Machine Learning and IoT

Water plays a fundamental position within side the existence of each organism. Without water no existence may want to survive and plenty of critical and non-critical human activities might now no longer be feasible without using wholesome watersheds. In addition, the process of monitoring and identifying its quality is very important which prevents people from various diseases and other health-related issues. To accomplish this, the proposed research work develops a framework for automating the process to predict water quality. This is achieved by integrating Machine learning techniques with the Internet of Things (IoT), where various IoT sensors are used to collect the environmental parameters related to water such as temperature, pH, and turbidity and which will be analyzed using Machine Learning techniques. Random Forest algorithm and Navie-based algorithm are utilized for predicting water quality and these results will be notified to the users on exemptional circumstances. The proposed framework helps people to analyze and monitor the quality of water without any human intervention.