A neural network based ionospheric model for the bottomside electron density profile over Grahamstown, South Africa
- Authors: McKinnell, L A
- Date: 2003
- Subjects: Neural networks (Computer science) Ionospheric electron density -- South Africa -- Grahamstown
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:5477 , http://hdl.handle.net/10962/d1005262
- Description: 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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- Date Issued: 2003
A new empirical model for the peak ionospheric electron density using neural networks
- Authors: McKinnell, L A
- Date: 1997
- Subjects: Ionospheric electron density Neural networks (Computer science)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5478 , http://hdl.handle.net/10962/d1005264
- Description: 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.
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- Date Issued: 1997