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

This project presents an efficient automated water quality assessment system that integrates IoT sensors and machine learning to monitor and analyse water parameters in real-time. Key water quality indicators such as Total Dissolved Solids (TDS), turbidity, temperature (via Dallas sensor), and pH are continuously measured using dedicated sensors. These sensor readings are transmitted to a Python-based system for data processing and prediction using a trained machine learning algorithm. The algorithm classifies the water quality and detects any abnormalities. If any parameter deviates from the safe threshold, an alert is triggered—activating a buzzer and sending a notification via the connected Arduino. Additionally, the processed data and prediction results are uploaded to the Thing Speak IoT webserver for remote monitoring and visualization. This system provides a reliable, real-time solution for water quality monitoring, ensuring timely alerts and accessible data through IoT integration.