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@article{194427,
        author = {Dr. M. MOHAMED ZAMAM NAZAR and K.A. USAIF AHAMED and N. NAGOOR MEERA},
        title = {Analysis of Water Quality in Rural Drinking River Systems Using IoT Sensors via Machine Learning Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {3638-3644},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194427},
        abstract = {This work suggests an IoT-supported water purity monitoring system for rural waterway systems, where the water is used for public use. The system deploys a network of sensors in stream locations to monitor various water quality parameters, including turbidity, pH, dissolved oxygen, and temperature. The data from these sensors are periodically gathered and sent to a focal point, which shapes and examines the data using the XGBoost machine learning computation. The system indicates to supply real-time water quality expectations and alarms, ensuring safe drinking water for rural communities. The use of machine learning models, specifically XGBoost, helps for precise prediction of water quality levels, based on a collection of sensor data.},
        keywords = {Water purity, IoT sensors, XGBoost, machine learning, rural water quality monitoring.},
        month = {March},
        }