Validation of a neural network based model to predict foF2

Abstract

This work presents the application of Neural Networks (NNs) techniques to develop an empirical model for foF2, the critical frequency of the F2 ionospheric layer. Prediction of foF2 parameter is important because of the paucity of data in some areas of the globe (due to uneven distributions of ground- based measuring equipment) and, also, because of the roles it plays in high frequency communications and navigation purposes. Hourly values of foF2 corresponding to the period 1985–2007 and obtained from 105 World-wide distributed ionospheric stations, have been considered in this work. A statistical analysis of the difference between NN model-derived and the corresponding experimental foF2 values was carried out for eleven (11) selected ionospheric stations during periods of very high (year 2000), relatively low (year 2006) and very low (year 2008) solar activity. In particular, the ionospheric behavior during the years 2000 and 2008 appeared to be very challenging to be captured for any empirical model. For comparison purposes, the same kind of statistical analysis was performed with foF2 values as obtained from the International Reference Ionosphere (IRI) model and from NeQuick 2 model. Results of the statistical comparison confirmed that the neural network modelling approach is suitable to describe in a climatological way the main characteristics of the ionosphere F2 layer peak frequency, also over the extended geographic region where the additional sensor stations were located. The results obtained also indicate that the NN predictions and NeQuick 2 algorithm driven by solar flux (F10.7) index have similar performance.