Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/1491
AuthorsCander, L. R. 
TitleArtificial neural network applications in ionospheric studies
Issue DateNov-1998
Series/Report no.41/5_6
URIhttp://hdl.handle.net/2122/1491
Keywordselectromagnetic waves
ionospheric modelling
prediction
forecasting
artificial neural networks
time series analysis
Subject Classification01. Atmosphere::01.02. Ionosphere::01.02.06. Instruments and techniques 
AbstractThe ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC). Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.
Appears in Collections:Annals of Geophysics

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