Published Paper Details:

This paper proposes a Smart Industrial Real-Time Water Quality Monitoring and Prediction System that integrates the Internet of Things (IoT) and machine learning to improve industrial water management and environmental safety. The system monitors key water parameters -Total Dissolved Solids (TDS), ammonia concentration, pH, turbidity, and temperature via dedicated sensors connected to an Arduino microcontroller, with data transmitted to the Thing-Speak cloud platform using an ESP8266 Wi-Fi module. Real-time alerts are facilitated through an on-site buzzer and a Telegram bot to notify users of abnormal conditions. For predictive analytics, the system employs machine learning algorithms such as Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XG-Boost, Logistic Regression, and Decision Tree to classify water quality status based on historical data. This unified framework provides a scalable and cost-effective solution for continuous monitoring, early warning, and data-driven decision-making across industries such as manufacturing, agriculture, and wastewater treatment.