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@article{186788,
        author = {Aditya Ajit  Mungase and Pratik Pradip Lokare and Sahil Sanjay Dendge and Dr. Bhausaheb Shinde},
        title = {Early Cloud Burst Detection System (API & Machine Learning)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1885-1890},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186788},
        abstract = {Cloudbursts those sudden, heavy downpours that drench a tiny area in minutes are a nightmare to predict and even harder to manage. This paper lays out a new system that mixes APIs and machine learning for spotting cloudbursts early. The setup pulls in real-time data from all over: open weather APIs, radar, IoT sensors you name it. Then, advanced ML models crunch the numbers to deliver fast, local predictions. The system isn’t just about forecasts, though. It sends out automatic alerts, keeps detailed logs, and comes with easy-to-use visualization tools for folks working in disaster management, city planning, farming, and more. We dig into the existing research, explain how the system works, walk through the methods, look at possible results, discuss what still needs work, and suggest where this could go next.},
        keywords = {Cloudburst, Machine Learning, Weather APIs, Disaster Management, Early Warning, Urban Flooding, Sensors, LSTM, Data Logging},
        month = {November},
        }