Preliminary Modelling of Solar Quiet Geomagnetic Field Average using Non-Linear Autoregressive with Exogeneous Input (NARX)

Abstract

This paper discusses geomagnetic field attempt modelling using an Artificial Neural Network (ANN). The local horizontal component of geomagnetic field data was collected on April 2011 (equinox) during a solar quiet day at recent solar cycle inclination-24 using the Magnetic Data Acquisition System (MAGDAS) in Langkawi, Malaysia, in the low latitude region. The calculated average values (mean) of the H component geomagnetic field variation during Equinox 2011 characterised the dominant geomagnetic field during that particular solar cycle. The difference in amplitude of maximum and minimum values shows a regular diurnal variation of the geomagnetic field during Sq inthe low latitude region. The output training utilised these calculated mean values during the modelling attempt. Meanwhile, the input training utilised proton density, solar wind plasma speed, plasma flow pressure, and Interplanetary Magnetic Field (IMF) space data using Non-Linear Auto Regressive Input (NARX). The best modelling outputs were 3, 2, and 3 for input delay (𝑛𝑢), output delay (𝑛y), and hidden layer size (h), respectively. Residual test and model fit analysis show an unbiased and high overlap between predicted and actual geomagnetic field average values, suggesting the model can potentially anticipate the geomagnetic field average during solar quiet in November (month of Equinox) on solar cycle inclination.