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@article{175017,
        author = {Geetha Kiran A and H G Pratheek Gowda and H N Nisarga and Pruthvi Prashanth Mattihali and Bhoomika G K},
        title = {Hydrosense: IoT-Based Smart Water Quality Monitoring System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1434-1440},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175017},
        abstract = {Due to the rising importance and necessity of monitoring water quality in real time, there is a need to develop more optimally efficient, scalable technologies. This paper provides a development of a new IoT-based smart water quality monitoring system that comprises new-age sensors with an ESP32 microcontroller for data collection. Water parameters include pH, turbidity, and temperature, which are collected, checked, and recorded, then sent to a cloud server for analysis. The system is designed using machine learning, specifically the random forest algorithm, which provides a very accurate prediction of contamination level and also the predictive maintenance duration. Web interface capabilities such as alarms and information about water quality upset that may indicate a need for the user to take action. In comparison to the domestic filters or large central utilities, this approach also provides forecasts and can be monitored, which increases its effectiveness and dependability substantially. The system architecture has been designed to make it extendible enough to be easily deployed to a broad variety of settings, from the developed cities to rural areas.},
        keywords = {IoT, Water Quality Monitoring, Machine Learning, Random Forest, Predictive Maintenance, Cloud Computing, Sensors, Real-time Monitoring},
        month = {April},
        }