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}
}
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… 

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