  Predicting geomagnetic conditions based on in-situ solar wind observations
allows us to evade disasters caused by large electromagnetic disturbances
originating from the Sun to save lives and protect economic activity. In this
study, we aimed to examine the relationship between the Kp index, representing
global magnetospheric activity level, and solar wind conditions using an
interpretable neural network known as potential learning (PL). Data analyses
based on neural networks are difficult to interpret; however, PL learns by
focusing on the "potentiality of input neurons" and can identify which inputs
are significantly utilized by the network. Using the full advantage of PL, we
extracted the influential solar wind parameters that disturb the magnetosphere
under southward Interplanetary magnetic field (IMF) conditions. The input
parameters of PL were the three components of the IMF (Bx, By, -Bz(Bs)), solar
wind flow speed (Vx), and proton number density (Np) in geocentric solar
ecliptic (GSE) coordinates obtained from the OMNI solar wind database between
1998 and 2019. Furthermore, we classified these input parameters into two
groups (targets), depending on the Kp level: Kp = 6- to 9 (positive target) and
Kp = 0 to 1+ (negative target). Negative target samples were randomly selected
to ensure that numbers of positive and negative targets were equal. The PL
results revealed that solar wind flow speed is an influential parameter for
increasing Kp under southward IMF conditions, which was in good agreement with
previous reports on the statistical relationship between the Kp index and solar
wind velocity, and the Kp formulation based on the IMF and solar wind plasma
parameters. Based on this new neural network, we aim to construct a more
correct and parameter-dependent space weather forecasting model.
