  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 ($R^2$) 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 2014-22 at a resolution of 1sec, and transform it from a time-series
into a 6-dimensional space with a corresponding EPB $R^2$ (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.
