CME Detection & Space Weather Monitoring System

Made for Team Digi Shakti - Smart India Hackathon (SIH)

A comprehensive real-time space weather monitoring and Coronal Mass Ejection (CME) detection system with predictive analytics. This system provides real-time monitoring, detection, and forecasting of space weather events including CMEs, geomagnetic storms, and solar activity.

🌟 Features

Real-Time Monitoring

Live Space Weather Data: Real-time monitoring of 15+ space weather parameters
CME Detection: Automated detection of Coronal Mass Ejections using machine learning
Geomagnetic Storm Tracking: Live geomagnetic indices (Kp, DST, Ap, F10.7)
Solar Activity Monitoring: Solar flares, sunspot numbers, and solar wind parameters

Predictive Analytics

7-Day Forecast: Multi-parameter space weather forecasting using LSTM models
CME Arrival Prediction: Time and direction prediction for CME events
Geomagnetic Storm Prediction: Storm intensity and timeline forecasting
Composite Index: Unified space weather index using Principal Component Analysis (PCA)

Advanced Features

Satellite Field Data Prediction: Match satellite coordinates with NOAA wind data for CME probability assessment
3D Visualizations: Interactive 3D animations for space weather parameters
Data Import/Export: Support for CDF file uploads and CSV exports
Historical Analysis: View and analyze past CME events and space weather data

🛠️ Tech Stack

Backend

FastAPI: High-performance Python web framework
Machine Learning: scikit-learn, scipy for CME detection algorithms
Data Processing: pandas, numpy for data manipulation
Database: PostgreSQL with SQLAlchemy ORM
Real-time Data: Integration with NOAA Space Weather Prediction Center APIs

Frontend

React 18: Modern UI framework with TypeScript
Vite: Fast build tool and dev server
Framer Motion: Smooth animations and transitions
Three.js: 3D visualizations and space weather animations
Recharts: Real-time data visualization
Shadcn UI: Beautiful, accessible component library
Tailwind CSS: Utility-first CSS framework

📋 Prerequisites

Python 3.11+
Node.js 18+ and npm
PostgreSQL (optional, for database features)
Git

🚀 Installation

1. Clone the Repository

git clone https://github.com/deepaksinghh12/Cme-detection-.git
cd Cme_detection_Phased2

2. Backend Setup

cd backend

# Create virtual environment (recommended)
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

3. Frontend Setup

cd frontend

# Install dependencies
npm install

🏃 Running the Application

Start Backend Server

cd backend
python main.py

Backend will run on http://localhost:8002

Start Frontend Server

cd frontend
npm run dev

Frontend will run on http://localhost:8080

📖 Usage

Main Dashboard

Access the main dashboard at http://localhost:8080 for an overview of:

Real-time space weather metrics
Recent CME events
System status and health monitoring
Quick access to all features

Phase-Based Navigation

The application is organized into phases:

Phase 1 (/phase1): Live Space Weather Data
- Real-time monitoring of 15+ parameters
- 4-grid layout with graphs, animations, and safety analysis
- Auto-refresh every 60 seconds
Phase 2 (/phase2): CME Prediction
- Arrival time prediction
- Direction forecasting
- Forecast visualizations
Phase 3 (/phase3): Live Geomagnetic Storm
- Real-time geomagnetic monitoring
- Storm intensity tracking
- Current storm effects
Phase 4 (/phase4): Geomagnetic Storm Prediction
- Time regression models
- Storm intensity prediction
- Future timeline visualization
Phase 5 (/phase5): Video & Image Animation
- Combined CME + Storm animations
- Video generation
- Image export capabilities
Field Data Prediction (/phase): Satellite CME Probability
- Select satellite by NORAD ID
- Match coordinates with NOAA wind data
- Calculate CME occurrence probability

Recent CME Events

View all recent CME events at /recent-cme-events with detailed analysis and detection results.

🔌 API Endpoints

Data Endpoints

GET /api/data/summary - Get data summary and system status
GET /api/data/realtime - Get real-time solar wind data
GET /api/data/particle - Get particle data
POST /api/data/upload - Upload CDF file for analysis

CME Detection

GET /api/cme/recent - Get recent CME events
POST /api/ml/analyze-cdf - Analyze CDF file for CME detection

Geomagnetic Data

GET /api/geomagnetic/storm/live - Get live geomagnetic indices
GET /api/forecast/predictions - Get 7-day forecast predictions

Satellite Data

GET /api/satellites - Get list of satellites
GET /api/satellites/{norad_id} - Get satellite details
GET /api/satellites/{norad_id}/cme-prediction - Get CME probability for satellite

NOAA Integration

GET /api/noaa/alerts - Get space weather alerts
GET /api/noaa/solar-flares - Get solar flare data
GET /api/noaa/images/{source} - Get image sequences

📁 Project Structure

Cme_detection_Phased2/
├── backend/
│   ├── main.py                 # FastAPI application
│   ├── noaa_realtime_data.py   # NOAA data fetcher
│   ├── database.py             # Database models
│   ├── scripts/                # ML models and scripts
│   └── requirements.txt        # Python dependencies
│
├── frontend/
│   ├── src/
│   │   ├── pages/              # Page components
│   │   ├── components/         # Reusable components
│   │   ├── lib/                # Utilities and API client
│   │   └── App.tsx             # Main app component
│   ├── package.json            # Node dependencies
│   └── vite.config.ts          # Vite configuration
│
└── README.md                   # This file

🎯 Key Features Explained

CME Detection Algorithm

The system uses a multi-parameter approach to detect CME events:

Velocity Threshold: Detects high-speed solar wind streams (>600 km/s)
Density Spikes: Identifies sudden density increases
Temperature Anomalies: Monitors proton temperature variations
Bz Component Analysis: Tracks southward magnetic field (Bz < -10 nT)
Composite Scoring: Weighted combination of all parameters

Forecast Model

Training Data: 29 years of historical space weather data
Model Type: LSTM (Long Short-Term Memory) neural network
Parameters: DST, Kp, Ap, Sunspot Number
Accuracy: 97.3% overall accuracy
Forecast Period: 7 days ahead

Composite Index

A unified space weather index combining:

DST Index (Disturbance Storm Time)
Kp Index (Planetary K-index)
Ap Index (Daily geomagnetic activity)
Sunspot Number Uses Principal Component Analysis (PCA) for optimal parameter combination.

🔧 Configuration

Backend Configuration

Edit backend/config.yaml for:

Database connection settings
NOAA API endpoints
Model parameters
Detection thresholds

Frontend Configuration

Edit frontend/src/lib/api.ts to configure:

API base URL
Request timeouts
Retry policies

🐛 Troubleshooting

Backend Issues:
- Port 8002 already in use: Change port in main.py or kill the process.
- Database connection errors: Ensure PostgreSQL is running (if using database).
- Missing dependencies: Run pip install -r requirements.txt.
Frontend Issues:
- Port 8080 already in use: Vite will automatically use next available port.
- Module not found: Run npm install to install dependencies.
- Build errors: Check Node.js version (requires 18+).

📊 Data Sources

NOAA Space Weather Prediction Center: Real-time solar wind and geomagnetic data
NASA OMNIWeb: Historical space weather datasets
Aditya L1: Solar observation data (when available)
External Satellite API: Satellite position and telemetry data

🤝 Contributing

Contributions are welcome! Please follow these steps:

Fork the repository
Create a feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

📝 License

This project is developed for Smart India Hackathon (SIH) presentation.

👥 Development Team