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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.
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Predicting Solar Proton Events of Solar Cycles 22–24 Using GOES Proton and Soft-X-Ray Flux Features
Abstract Solar energetic particle (SEP) events and their major subclass, solar proton events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only solar cycle (SC) 24 in our previous study, we found that it may be sufficient to only utilize proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording ≥10 MeV ≥10 particle flux unit SPEs with their properties, spanning SCs 22–24, using NOAA’s Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a support vector machine (SVM) and extreme gradient boosting (XGBoost). We explore the effects of training models with data from oneandtwo SCs, evaluating how transferable a model might be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while also outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker true skill statistic and Heidke skill scores2, even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes toward validating forecasts using long-spanning data—an understudied area in SEP research that should be considered to verify the cross cycle robustness of ML-driven forecasts.
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- PAR ID:
- 10500948
- Publisher / Repository:
- IOPSCIENCE
- Date Published:
- Journal Name:
- The Astrophysical Journal Supplement Series
- Volume:
- 270
- Issue:
- 1
- ISSN:
- 0067-0049
- Page Range / eLocation ID:
- 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3847/1538-4365/ad0a6c
