WaterProbe: Real-Time Water Quality Analytics using Machine Learning and IoT

Abstract

This paper presents the design and implementation of a new water quality monitoring system that integrates Machine Learning (ML) algorithms with Internet of Things (IoT) technology. The system was designed to solve the challenges of traditional water quality monitoring methods by providing real-time, accurate and automated monitoring capabilities.

The ML algorithms utilize historical data and real-time sensor readings to identify patterns, trends, and anomalies in water quality parameters. By continuously learning from incoming data, the system can adjust and improve its predictive capabilities over time, improving the accuracy of water quality measurements and early warning systems.

The system includes IoT sensors deployed in water bodies to measure water quality parameters such as pH and TDS temperature. These sensors transmit data, whereas ML algorithms are sent for data analysis and anomaly detection.

Implementing this ML-enabled IoT system offers several advantages, including proactive detection of water quality issues, reduced manual intervention, and optimized resource allocation for environmental monitoring organizations and water utilities.