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@article{172449,
        author = {Hamini.T and Kaviya.K and Subasri.K and Sushmitha.S and Senthazhai.S},
        title = {DESIGN OF MCNN FOR IoT BASED SMART WATER QUALITY MONITORING AND PREDICTION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {3209-3213},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172449},
        abstract = {The demand for clean and safe water has driven the creation of sophisticated water quality monitoring systems. This project introduces an IoT-based Smart Water Quality Monitoring and Prediction System employing a Multiscale Convolutional Neural Network (MCNN). By utilizing IoT-enabled sensors, the system continuously measures critical water parameters such as pH, temperature, turbidity, and dissolved oxygen, facilitating real-time data collection for comprehensive analysis. Additionally, the use of MCNN allows for the prediction of future water quality trends using historical data, offering early alerts for potential contamination. Integrating Raspberry Pi Pico with IoT nodes enhances real-time monitoring, while deep learning ensures accurate predictions, making this approach scalable and effective for applications in agriculture, drinking water supply, and environmental monitoring to promote safe and sustainable water management.},
        keywords = {IoT, Smart Water Quality Monitoring, Multiscale Convolutional Neural Network, Raspberry Pi Pico, real-time data collection},
        month = {January},
        }