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@article{179228,
        author = {Raghavendra TS and Sunkesula Munnera Begum and Gowtham Reddy P and Varun Sannidhi and Niveda  Sudeep},
        title = {Cloudburst Prediction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6612-6616},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179228},
        abstract = {Cloudbursts are sudden and intense
downpours of rain that often strike without warning,
causing flash floods, landslides, and significant
damage—particularly in mountainous or densely
populated areas. Predicting these events accurately and
in time is essential to minimize their impact and save
lives. This project introduces a Cloudburst Prediction
System designed to monitor and analyze real-time
weather data, satellite images, and key atmospheric
indicators like pressure, humidity, temperature, and wind
speed. By applying advanced machine learning
techniques, the system can detect patterns and provide
early warnings of potential cloudburst events. It also
features an intuitive interface that delivers timely alerts
and visual insights to support quick decision-making for
both authorities and the public. With this system, we aim
to strengthen disaster preparedness and reduce the risks
associated with sudden extreme weather events.},
        keywords = {Cloudburst, Early Warning System, Machine Learning, Real-time Weather Data, Disaster Risk Reduction, Flood Alerts},
        month = {May},
        }