| Summary: | This thesis describes the development and application of a neural network based ionospheric model for the bottomside electron density profile over Grahamstown, South Africa. All available ionospheric data from the archives of the Grahamstown (33.32ºS, 26.50ºE) ionospheric station were used for training neural networks (NNs) to predict the parameters required to produce the final profile. Inputs to the model, called the LAM model, are day number, hour, and measures of solar and magnetic activity. The output is a mathematical description of the bottomside electron density profile for that particular input set. The two main ionospheric layers, the E and F layers, are predicted separately and then combined at the final stage. For each layer, NNs have been trained to predict the individual ionospheric characteristics and coefficients that were required to describe the layer profile. NNs were also applied to the task of determining the hours between which an E layer is measurable by a groundbased ionosonde and the probability of the existence of an F1 layer. The F1 probability NN is innovative in that it provides information on the existence of the F1 layer as well as the probability of that layer being in a L-condition state - the state where an F1 layer is present on an ionogram but it is not possible to record any F1 parameters. In the event of an L-condition state being predicted as probable, an L algorithm has been designed to alter the shape of the profile to reflect this state. A smoothing algorithm has been implemented to remove discontinuities at the F1-F2 boundary and ensure that the profile represents realistic ionospheric behaviour in the F1 region. Tests show that the LAM model is more successful at predicting Grahamstown electron density profiles for a particular set of inputs than the International Reference Ionosphere (IRI). It is anticipated that the LAM model will be used as a tool in the pin-pointing of hostile HF transmitters, known as single-site location.
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