AI-Based Rainfall Prediction Using Meteorological Satellite Data

Abstract

Artificial Intelligence (AI) based rainfall prediction using meteorological satellite data is an advanced weather forecasting system designed to improve the accuracy and reliability of rainfall prediction. Traditional rainfall forecasting methods mainly depend on numerical weather prediction models and ground-based observations, which often struggle to capture complex atmospheric variations and localized rainfall events. This project addresses these limitations by utilizing meteorological satellite imagery combined with Artificial Intelligence and Machine Learning techniques. Satellite data provides continuous information about cloud movement, atmospheric moisture, temperature, humidity, wind speed, and other environmental parameters over large geographical regions. The collected data undergoes preprocessing, feature extraction, and normalization before being used for model training. Machine Learning algorithms such as Random Forest, Decision Tree, and Gradient Boosting are employed to identify complex weather patterns and predict rainfall occurrence with improved precision. A Flask-based web application is developed to provide an interactive where users can upload meteorological data and receive rainfall predictions instantly. The proposed system enhances forecasting accuracy, reduces computational complexity, supports real-time weather monitoring, and assists farmers, disaster management authorities, aviation services, and water resource planners in making informed decisions.