A New Machine Learning Approach for Predicting Extreme Space Weather

We present an innovative method, ProBoost (Probabilistic Boosting), for forecasting extreme space weather events using ensemble machine learning (ML). Ensembles enhance prediction accuracy, but applying them to ML faces challenges as ML models often lack wellcalibrated uncertainty estimates. Moreover, space weather problems are typically affected by very imbalanced datasets (i.e., extreme and rare events) To overcome these difficulties, we developed a method that incorporates uncertainty quantification (UQ) in neural networks, enabling simultaneous forecasting of prediction uncertainty.
Our study applies ProBoost to the following space weather applications:
• One-to-Six-Hour Lead-Time Model: Predicting Disturbance Storm Time (Dst) values using solar wind data.
• Two-Day Lead-Time Model: Forecasting Dst probability using solar images.
• Geoelectric Field Model: Multi-hour lead time, incorporating solar wind and SuperMag data.
• Ambient Solar Wind Velocity Forecast: Up to 5 days ahead.
ProBoost is model-agnostic, making it adaptable to various forecasting applications beyond space weather.