• DOI:10.48550/arXiv.2209.13482
• Corpus ID: 252545039

Predicting Swarm Equatorial Plasma Bubbles Via Supervised Machine Learning

@article{Reddy2022PredictingSE,
  title={Predicting Swarm Equatorial Plasma Bubbles Via Supervised Machine Learning},
  author={S Suseela Reddy and Colin Forsyth and A. L. Aruliah and Dhiren Kataria and A. R. A. Smith and Jacob Bortnik and E. Aa and G. Lewis},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.13482}
}

• S. Reddy, C. Forsyth, +5 authors G. Lewis
• Published 27 September 2022
• Physics
• ArXiv

The prediction has an accuracy of 88% and performs well across the EPB specific spatiotemporal scales. This proves that the XGBoost method is able to successfully capture the climatological and daily variability of SWARM EPBs. Capturing the daily variance has long evaded researchers because of local and stochastic features within the ionosphere. We take advantage of Shapley values to explain the model and to gain insight into the physics of EPBs. We find that as the solar wind speed increases the probability of an EPB decreases. We also identify a spike in EPB probability around the Earth-Sun perihelion. Both of these insights were derived directly from the XGBoost and Shapley technique.