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.

Related documents:

This repository does not currently have the full-text of this item.
You may be able to access a copy if URLs are provided below.


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.


Item Type Articles
CreatorsChan, A. H. Y.and Cannon, P. S.
DepartmentsFaculty of Engineering & Design > Electronic & Electrical Engineering
ID Code6110
Additional InformationID number: ISI:000177402900014


Actions (login required)

View Item