An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data

Abstract Solar energetic particle (SEP) events, in particular high-energy-range SEP events, pose significant risks to space missions, astronauts, and technological infrastructure. Accurate prediction of these high-impact events is crucial for mitigating potential hazards. In this study, we present an end-to-end ensemble machine learning (ML) framework for the prediction of high-impact ∼100 MeV SEP events. Our approach leverages diverse data modalities sourced from the Solar and Heliospheric Observatory and the Geostationary Operational Environmental Satellite integrating extracted active region polygons from solar extreme ultraviolet (EUV) imagery, time-series proton flux measurements, sunspot activity data, and detailed active region characteristics. To quantify the predictive contribution of each data modality (e.g., EUV or time series), we independently evaluate them using a range of ML models to assess their performance in forecasting SEP events. Finally, to enhance the SEP predictive performance, we train an ensemble learning model that combines all the models trained on individual data modalities, leveraging the strengths of each data modality. Our proposed ensemble approach shows promising performance, achieving a recall of 0.80 and 0.75 in balanced and imbalanced settings, respectively, underscoring the effectiveness of multimodal data integration for robust SEP event prediction and enhanced forecasting capabilities.