A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data

This study focuses on modeling the ionospheric electron density over a 5 degree rectangular African equatorial region centered in Kano, Nigeria, using artificial neural network. The electron density measurements data used in this research were obtained from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) team covering the period of 16 years ranging from 2006 to 2021 through the method of radio occultation. The data is in <i>ionPrf</i> file format provided by the COSMIC team. The profiles are in <i>netCDF</i> format containing ionospheric information including electron density profiles. The data were collected from a 5-degree rectangular African equatorial region centered in Kano, Nigeria (geographic coordinates: 12.00<sup>o</sup>N, 8.59<sup>o</sup>E; geomagnetic coordinates: 0.47<sup>o</sup>N). Computer neural network was used in training the data using the Levenberg Marquardt (LM) algorithm. Results for 22 trained networks demonstrated that the network with nineteen hidden layer neurons gave the most accurate result with root-mean square error (RMSE) of 86 𝗑 10<sup>3</sup> electrons per cubic centimeter. The criteria was that the best network is the one that gives the highest minimization of RMSE. The nineteenth network was therefore finally used as the neural network (NN) electron density model. The NN model’s hourly binned electron density predictions was compared with those of the NeQuick and International Reference Ionosphere (IRI) models. The result of the comparison illustrated that out of the 423 investigated ionosonde profiles, the NN model performed best in 200 profiles (approximately 47%) followed by the IRI which performed best in 114 profiles (approximately 27%) while the NeQuick performed best in 109 profiles (approximately 26%). The developed NN model was demonstrated to be able to reproduce the different ionospheric features (diurnal variations, seasonal variations, and the long-term changes).