Reddy, SA;
Forsyth, C;
Aruliah, A;
Smith, A;
Bortnik, J;
Aa, E;
Kataria, DO;
(2023)
Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values.
Journal of Geophysical Research: Space Physics
, 128
(6)
, Article e2022JA031183. 10.1029/2022ja031183.
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Abstract
In this study we present AI Prediction of Equatorial Plasma Bubbles (APE), a machine learning model that can accurately predict the Ionospheric Bubble Index (IBI) on the Swarm spacecraft. IBI is a correlation (R2) between perturbations in plasma density and the magnetic field, whose source can be Equatorial Plasma Bubbles (EPBs). EPBs have been studied for a number of years, but their day-to-day variability has made predicting them a considerable challenge. We build an ensemble machine learning model to predict IBI. We use data from 2014 to 2022 at a resolution of 1s, and transform it from a time-series into a 6-dimensional space with a corresponding EPB R2 (0–1) acting as the label. APE performs well across all metrics, exhibiting a skill, association and root mean squared error score of 0.96, 0.98 and 0.08 respectively. The model performs best post-sunset, in the American/Atlantic sector, around the equinoxes, and when solar activity is high. This is promising because EPBs are most likely to occur during these periods. Shapley values reveal that F10.7 is the most important feature in driving the predictions, whereas latitude is the least. The analysis also examines the relationship between the features, which reveals new insights into EPB climatology. Finally, the selection of the features means that APE could be expanded to forecasting EPBs following additional investigations into their onset.
| Type: | Article |
|---|---|
| Title: | Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1029/2022ja031183 |
| Publisher version: | https://doi.org/10.1029/2022ja031183 |
| Language: | English |
| Additional information: | ©2023. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10171030 |
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