  Geomagnetic storms, disturbances of Earth's magnetosphere caused by masses of
charged particles being emitted from the Sun, are an uncontrollable threat to
modern technology. Notably, they have the potential to damage satellites and
cause instability in power grids on Earth, among other disasters. They result
from high sun activity, which are induced from cool areas on the Sun known as
sunspots. Forecasting the storms to prevent disasters requires an understanding
of how and when they will occur. However, current prediction methods at the
National Oceanic and Atmospheric Administration (NOAA) are limited in that they
depend on expensive solar wind spacecraft and a global-scale magnetometer
sensor network. In this paper, we introduce a novel machine learning and
computer vision approach to accurately forecast geomagnetic storms without the
need of such costly physical measurements. Our approach extracts features from
images of the Sun to establish correlations between sunspots and geomagnetic
storm classification and is competitive with NOAA's predictions. Indeed, our
prediction achieves a 76% storm classification accuracy. This paper serves as
an existence proof that machine learning and computer vision techniques provide
an effective means for augmenting and improving existing geomagnetic storm
forecasting methods.
