SOLARIS-X

🛰️ SOLARIS-X: Advanced Space Weather Prediction System

World-class machine learning system for geomagnetic storm prediction using 29 years of space weather data

🌟 OVERVIEW

SOLARIS-X is an advanced ensemble machine learning system designed for real-time geomagnetic storm prediction. Built with 29 years of space weather data (1996-2025) and 79 physics-informed features, it achieves 96.5% AUC with superior storm detection capabilities.

🎯 Key Achievements

🏆 67.8% Storm Recall - Catches 2 out of 3 geomagnetic storms
⚡ <100ms Inference - Real-time prediction capability
🔬 Physics-Informed - Features based on solar wind & magnetospheric physics
🚀 Production-Ready - Complete MLOps pipeline with deployment architecture

📊 PERFORMANCE METRICS

Model	AUC	Precision	Recall	F1-Score	Best For
Meta-Ensemble ⭐	0.9646	0.4862	0.6784	0.5664	Storm Detection
LightGBM Baseline	0.9671	0.6655	0.4904	0.5647	High Precision

🎖️ Meta-Ensemble selected for production - Superior storm detection critical for space weather operations

🏗️ SYSTEM ARCHITECTURE

🔬 Core Components

Advanced Feature Engineering
- 29-year dataset (257,232 samples)
- 79 physics-based predictors
- Temporal lag features (1-24 hours)
- Solar cycle & seasonal patterns
Ensemble Architecture
- LightGBM: Gradient boosting baseline
- Bidirectional GRU: Temporal sequence modeling
- Meta-Learner: Combines predictions optimally
Production Pipeline
- Memory-optimized data processing
- Robust scaling & preprocessing
- Model persistence & versioning
- Real-time inference capability

📈 DATASET & FEATURES

🛰️ Space Weather Data Sources

NASA OMNI Database: Solar wind parameters, IMF data
NOAA SWPC: Geomagnetic indices (Kp, AE, Dst)
Temporal Coverage: 1996-2025 (29 years)
Data Quality: 98.3% completeness after cleaning

⚡ Top Physics Features

Kp-index - Geomagnetic activity indicator
IMF Magnitude - Interplanetary magnetic field strength
Plasma Beta - Solar wind plasma parameter
AE Index - Auroral electrojet activity
Solar Cycle Phase - Long-term solar variability

🚀 QUICK START

Installation

Clone repository

git clone https://github.com/yourusername/SOLARIS-X.git
cd SOLARIS-X

Create virtual environment

python -m venv venv

Activate virtual environment

#Windows:
venv\Scripts\activate

#Linux/Mac:
source venv/bin/activate

Install dependencies

pip install -r requirements.txt

Training Pipeline

Run complete training pipeline

python scripts/training/complete_pipeline.py

Individual model training

python scripts/training/test_lightgbm.py
python scripts/training/test_neural_network.py

Quick Prediction Example

import joblib
import pandas as pd
import numpy as np

Example space weather features
features = {
'Kp_index': 3.5,
'IMF_Magnitude_lag_12h': 7.2,
'Plasma_Beta': 0.8,
'AE_index': 150.0,
'Solar_Cycle_Phase': 0.6
# ... additional features required
}

print("SOLARIS-X Space Weather Prediction System")
print("Geomagnetic Storm Prediction: Ready for deployment")

📁 PROJECT STRUCTURE

Core Directories

📊 Data Pipeline

data/processed/features/ - Engineered feature datasets (excluded)
data/raw/omni/ - Original OMNI database files (excluded)

🤖 Training System

scripts/training/models/ - Individual model trainers (4 advanced models)
scripts/training/utils/ - Training utilities and base classes
scripts/training/complete_pipeline.py - Main orchestrator

💾 Model Management

models/checkpoints/ - Training checkpoints and metadata
models/trained/ - Production models (excluded)

📈 Results & Analysis

results/plots/ - Performance visualizations (10 charts)

📋 Configuration

requirements.txt - Python dependencies
.gitignore - Repository optimization
README.md - Documentation

Key Features

25+ Python modules with professional architecture
4 advanced ML models including meta-ensemble system
Complete MLOps pipeline with automated evaluation
Production-ready deployment configuration

🏗️ Architecture Highlights

Modular Design: Separated model trainers and utilities
Production Ready: Complete MLOps pipeline structure
Optimized Storage: Large files excluded via .gitignore
Comprehensive Evaluation: Visualization and metrics tracking
Professional Organization: Clear separation of concerns

Note: Files marked as “(excluded)” are not tracked in git due to size constraints but are generated during training.

🔬 SCIENTIFIC METHODOLOGY

🎯 Research Approach

Temporal Validation: Proper chronological train/validation/test splits
Physics-Informed: Features based on magnetospheric coupling theory
Imbalanced Learning: Specialized techniques for rare storm events
Ensemble Methods: Meta-learning for optimal prediction combination

📊 Evaluation Protocol

No Data Leakage: Strict temporal separation of datasets
Multiple Metrics: AUC, F1, Precision, Recall for comprehensive assessment
Cross-Validation: Robust performance estimation
Uncertainty Quantification: Prediction confidence intervals

🌍 APPLICATIONS

🛰️ Operational Use Cases

Space Weather Centers: NOAA, ESA integration
Satellite Operations: ISS, commercial satellite protection
Power Grid Safety: Geomagnetic storm early warning
Aviation: High-altitude flight safety alerts

🎓 Research Applications

Space Physics: Magnetospheric dynamics research
Climate Science: Space weather impact studies
Machine Learning: Rare event prediction techniques
Data Science: Time series ensemble methods

🔧 TECHNICAL SPECIFICATIONS

💻 System Requirements

Python: 3.8+
RAM: 8GB minimum, 16GB recommended
CPU: Multi-core processor (12+ cores optimal)
Storage: 5GB for full dataset and models

🛠️ Key Dependencies

lightgbm==4.6.0
tensorflow-cpu==2.20.0
scikit-learn==1.3.0
pandas==2.0.3
numpy==1.24.3
matplotlib==3.7.1
seaborn==0.12.2

👨‍💻 AUTHOR

Sumanth - Space Weather & Machine Learning Research

🌐 GitHub: @Sumanth1410-git
📧 Contact: sumanthp141005@gmail.com

🏆 ACHIEVEMENTS

🥇 96.5% AUC - World-class space weather prediction performance
🎯 67.8% Storm Recall - Superior rare event detection
🚀 Production-Ready - Complete MLOps pipeline
🔬 Physics-Informed - Scientifically validated approach
📊 29-Year Dataset - Comprehensive historical coverage

📄 LICENSE

This project is licensed under the MIT License.

🙏 ACKNOWLEDGMENTS

NASA OMNI Database - Space weather data provision
NOAA Space Weather Prediction Center - Operational data access
Space Weather Community - Research inspiration and validation
Open Source Contributors - Tool and library development

### 🌟 **SOLARIS-X: Protecting Earth from Space Weather** 🌟 *Built with ❤️ for the space weather research community* **[⭐ Star this repository](https://github.com/Sumanth1410-git/SOLARIS-X)** if it helps your research!