International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Based Cloudburst Prediction System Using Machine Learning & Deep Learning Techniques |
|---|---|
| ABSTRACT | Cloudbursts are sudden and intense rainfall events that can cause floods and landslides. This project presents an AI-Powered Global Cloudburst Prediction System developed as a real-time web application. The system uses weather data from the OpenWeatherMap API and analyzes parameters like temperature, humidity, pressure, windspeed, and rainfall intensity. A hybrid AI model using Logistic Regression, Random Forest-inspired logic, and LSTM improves prediction accuracy and risk analysis. The application provides interactive visualization, risk alerts, and early warning support for effective disaster preparedness and management. |
| AUTHOR | SWATHIKA T, REBHA P A, R.RAJA MONSINGH U.G. Student, Department of AI&DS, Christ the King Engineering College, Coimbatore, Tamil Nadu, India Associate Professor, Department of AI&DS, Christ the King Engineering College, Coimbatore, Tamil Nadu, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405035 |
| pdf/35_AI-Based Cloudburst Prediction System Using Machine Learning & Deep Learning Techniques.pdf | |
| KEYWORDS | |
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