Space Weather Prediction

Solar activity, such as solar flares and coronal mass ejections (CMEs), significantly impacts space weather, affecting satellites, power grids, and communication systems. Predicting these events is critical to preparing for and mitigating their effects. Our project leverages advanced machine learning models to analyze historical and real-time data, predicting solar flux and activity

We gather real-time solar activity data from two primary sources:

1. NOAA's Solar Activity Data API: This provides us with real-time measurements of solar flux, a key indicator of solar activity.
2. NASA's Helioviewer API (SDO Data): Using NASA’s Solar Dynamics Observatory (SDO) data, we access high-quality solar images and related metadata, enriching our dataset with valuable visual and contextual information about solar phenomena.

Our solution:

The Prediction Pipeline

Here’s an overview of how we convert raw data into actionable insights:

1. Real-Time Data Collection (NOAA + NASA’s SDO data)
2. Data Preprocessing (Scaling, Sequence Creation)
3. LSTM Model Training (Learning from Past Patterns)
4. Prediction of Future Solar Flux
5. Visualization and Output (Providing easy-to-understand predictions)