  Atmospheric neutral density is a crucial component to accurately predict and
track the motion of satellites. During periods of elevated solar and
geomagnetic activity atmospheric neutral density becomes highly variable and
dynamic. This variability and enhanced dynamics make it difficult to accurately
model neutral density leading to increased errors which propagate from neutral
density models through to orbit propagation models. In this paper we
investigate the dynamics of neutral density during geomagnetic storms. We use a
combination of solar and geomagnetic variables to develop three Random Forest
machine learning models of neutral density. These models are based on (1) slow
solar indices, (2) high cadence solar irradiance, and (3) combined high-cadence
solar irradiance and geomagnetic indices. Each model is validated using an
out-of-sample dataset using analysis of residuals and typical metrics. During
quiet-times, all three models perform well; however, during geomagnetic storms,
the combined high cadence solar irradiance/geomagnetic model performs
significantly better than the models based solely on solar activity. The
combined model capturing an additional 10\% in the variability of density and
having an error up to six times smaller during geomagnetic storms then the
solar models. Overall, this work demonstrates the importance of including
geomagnetic activity in the modeling of atmospheric density and serves as a
proof of concept for using machine learning algorithms to model, and in the
future forecast atmospheric density for operational use.
