Nonlinear forecasts of foF2: variation of model predictive accuracy over time
Chan, A. H. Y. and Cannon, P. S., 2002. Nonlinear forecasts of foF2: variation of model predictive accuracy over time. Annales Geophysicae, 20 (7), pp. 1031-1038.
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A nonlinear technique employing radial basis function neural networks (RBF-NNs) has been applied to the short-term forecasting of the ionospheric F2-layer critical frequency, foF2. The accuracy of the model forecasts at a northern mid-latitude location over long periods is assessed, and is found to degrade with time. The results highlight the need for the retraining and re-optimization of neural network models on a regular basis to cope with changes in the statistical properties of geophysical data sets. Periodic retraining and re-optimization of the models resulted in a reduction of the model predictive error by similar to0.1 MHz per six months. A detailed examination of error metrics is also presented to illustrate the difficulties encountered in evaluating the performance of various prediction/forecasting techniques.
|Creators||Chan, A. H. Y.and Cannon, P. S.|
|Departments||Faculty of Engineering & Design > Electronic & Electrical Engineering|
|Additional Information||ID number: ISI:000177402900014|
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