A new empirical model for the peak ionospheric electron density using neural networks

Abstract

This thesis describes the search for a temporal model for predicting the peak ionospheric electron density-(foF2). Existing models, such as the International Reference Ionosphere (IRI) and 8KYCOM, were used to predict the 12 noon foF2 value over Grahamstown (26°E, 33°8). An attempt was then made to find a model that would improve upon these results. The traditional method of linear regression was used as a first step towards a new model. It was found that this would involve a multi variable regression that is reliant on guessing the optimum variables to be used in the final equation. An extremely complicated modelling equation involving many terms would result. Neural networks (NNs) are introduced as a new technique for predicting foF2. They are also applied, for the first time, to the problem of determining the best predictors of foF2. This quantity depends upon day number, level of solar activity and level of magnetic activity. The optimum averaging lengths of the solar activity index and the magnetic activity index were determined by appling NNs, using the criterion that the best indices are those that give the lowest rms error between the measured and predicted foF2. The optimum index for solar activity was found to be a 2-month running mean value of the daily sunspot number and for magnetic activity a 2-day averaged A index was found to be optimum. In addition, it was found that the response of foF2 to magnetic activity changes is highly non-linear and seasonally dependent. Using these indices as inputs, the NN trained successfully to predict foF2 with an rms error of 0.946 MHz on the daily testing values. Comparison with the IRI showed an improvement of 40% on the rms error. It is also shown that the NN will predict the noon value of foF2 to the same level of accuracy for unseen data of the same type.