Geomagnetic storm prediction with input parameter ranking

Geomagnetic storms are multi-day events characterised by significant perturbations to the magnetic fieldthe Earth, driven by solar activity. Numerous efforts have been undertaken to utilise in-situ measurements of the solar wind plasma to predict perturbations to the geomagnetic field measured on the ground. Typically, solar wind measurements are used as input parameters to a regression problem tasked with predicting a perturbation index such as the 1-minute cadence symmetric-H (Sym-H) index.

However, we do not know exactly which of these parameters are the most important, and how their relative importance changes with storm phase. Feedforward neural networks provide the basis for complex regression models that produce accurate predictions in a variety of applications. However, they generally do not provide any information about the utility of each of the input parameters in terms of their contribution to model accuracy.

Therefore the primary aim of this project is to build a neural network topology capable of ranking the input parameters in terms of importance to the model. The secondary aim of this project is to build an adequate prediction model of SYM-H based on input parameters from solar wind plasma and magnetic field data, measured by satellites in space.

If satisfactory performance can be demonstrated this model will be deployed at the Regional Warning Centre of the SANSA in Hermanus.