AI-Powered CME Alerts: Enhance Space Weather PredictionsAI-Powered CME Alerts: Enhance Space Weather Predictions
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Halo CME Detection & Space Weather Prediction System
Title: ML-Based Early Warning System for Solar Storms (Smart India Hackathon Winner)
Problem: Geomagnetic storms and Coronal Mass Ejections (CMEs) threaten satellite infrastructure, but predicting their arrival and severity is a hard time-series forecasting problem with multi-scale temporal patterns.
My approach: I developed supervised time-series forecasting models (LSTM, GRU) trained on space-weather data, applying lag and rolling-window feature engineering to capture temporal dynamics across multiple scales. I also built a real-time ML-based alerting pipeline for risk assessment and forecasted key geomagnetic indices (Kp, Ap, Dst, Sunspot Number) 7 days ahead.
Tools used: Python, PyTorch/TensorFlow, LSTM, GRU, time-series feature engineering, MLflow
Result: Achieved a 7% improvement in CME/storm prediction accuracy and a 6% improvement in arrival-time prediction. Won Smart India Hackathon 2025 (Ministry of Education's Innovation Cell, Government of India), and the work was published as corresponding author in the Journal of Science, Computing and Engineering Research (JSCER), 2026.
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