IoT‐Enabled Machine Learning for Comprehensive Water Quality Assessment in the Mahanadi River: A Multibelt Analysis of Seasonal Contamination and Predictive Modeling

The increase in water contamination in the Mahanadi River, exacerbated by industrial discharges, domestic effluents, and agricultural runoff, requires urgent and advanced water quality monitoring. This research integrates IoT‐based monitoring systems with the powerful XGBoost machine learning model to address the limitations of traditional evaluation methods. The Mahanadi River, a vital resource amid rapid urbanization and industrialization, requires sustainable water quality management. Cutting‐edge technology facilitates real‐time data collection on pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total coliforms (TCs). The study delves into intricate relationships between variables, geographical regions, belts, and seasonal changes, providing a nuanced understanding of the dynamics of water pollution. Incorporating sophisticated data analysis and machine learning empowers precise predictions and comprehensive insights. A multibelt assessment across industrial, residential, and agricultural regions during various seasons offers a holistic perspective on water quality fluctuations. XGBoost demonstrates remarkable efficiency, achieving 95% accuracy in predicting water quality categories. Comparative evaluations highlight the superiority of the proposed method in seasonal patterns, the calculation of the water quality index (WQI), and belt‐wise comparisons. This research is crucial in developing effective management strategies and sustaining conservation efforts for the Mahanadi River ecosystem. It serves as a valuable resource for policymakers, conservationists, and concerned residents, offering insight into the future of the river and contributing to the broader discourse on environmental preservation.