Nonlinear forecasts of foF2: variation of model predictive accuracy over time

Details Nonlinear forecasts of foF2: variation of model predictive accuracy over time Nonlinear forecasts of foF2: variation of model predictive accuracy over time

Jul 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002angeo..20.1031c&link_type=abstract

Annales Geophysicae, vol. 20, Issue 7, pp.1031-1038

Computer Science

Performance

2

Scientific paper

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 reoptimization of neural network models on a regular basis to cope with changes in the statistical properties of geophysical data sets. Periodic retraining and reoptimization of the models resulted in a reduction of the model predictive error by ~ 0.1 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.

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