Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values

TL;DR

This paper introduces a machine learning model that accurately predicts Equatorial Plasma Bubbles using satellite data, with insights into feature importance and potential for future forecasting applications.

Contribution

The study develops an ensemble machine learning model for predicting EPBs and uses Shapley values to analyze feature importance, offering new climatological insights.

Findings

Model achieves high prediction accuracy with R^2 of 0.96

F10.7 solar activity index is the most influential feature

Model performs best post-sunset and during equinoxes

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 ($R^2$) 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.