Water Quality Monitoring & Forecasting System

Abstract

Water quality monitoring is essential for ensuring safe drinking water, environmental sustainability, and effective resource management. Traditional water quality assessment methods rely on manual sampling and laboratory testing, which are time-consuming and lack real-time insights. This project proposes an intelligent water quality monitoring and forecasting system that integrates Internet of Things (IoT) sensors with machine learning algorithms. The system continuously collects real-time data on parameters such as pH, turbidity, temperature, dissolved oxygen, and conductivity. These data are analyzed using machine learning models to detect anomalies and predict future water quality trends. The proposed approach improves accuracy, enables early detection of contamination, and supports proactive decision-making. Studies show that IoT-based monitoring combined with machine learning can achieve significantly higher accuracy compared to traditional methods and provide predictive insights for sustainable water management .