EDGE AI-BASED WATER QUALITY MONITORING, PREDICTION AND ALERT SYSTEM

Abstract

Safe and clean water is essential for public health, but regular water testing can be time-consuming and difficult to perform in remote areas. This paper presents a low-cost Edge AI-based water quality monitoring system using an ESP32 for real-time on-site monitoring. The system uses sensors to measure pH, Total Dissolved Solids (TDS), turbidity, temperature, and water level. The collected data is processed locally to calculate the Water Quality Index (WQI) and determine the overall water quality status. Based on predefined limits, the system provides immediate alerts using LEDs and a buzzer. The measured data and WQI results can also be transmitted through Wi-Fi to a web dashboard for remote monitoring and record keeping. Local processing reduces dependence on continuous internet connectivity and enables faster detection of abnormal water conditions. The proposed system provides a simple, affordable, and practical solution for real-time water quality monitoring in rural, domestic, and small-scale applications.