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The analysis of current neural network configuration used to predict the critical frequency FoF2 of the ionosphere
Авторы: Salimov B., Hmelnov A., Berngardt O.
Журнал: CEUR Workshop Proceedings: Proc. of 3rd Intern. Workshop on Data Life Cycle in Physics (DLC 2019; Irkutsk)
Том: 2406
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Год: 2019
Отчётный год: 2019
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Аннотация: Ionosphere is the ionized part of the upper atmosphere containing free electrons and ions. The critical frequency (foF2) is one of the most important parameters of the ionospheric electron density, which affects the functionality of radio communication and navigation equipment. This frequency is a very variable parameter, which depends on various geophysical parameters. Therefore, improving the accuracy of predicting the critical frequency foF2 of the ionosphere is of great importance for the proper operation of radio communication and navigation equipment. Various physical and empirical models are used to predict this parameter. The large amount of observational data and development of modern machine learning algorithms make it possible to use new approaches for predicting ionospheric parameters. In the paper we analyzed a neural network created for predicting foF2 measured by Irkutsk Digisonde, as a function of various geophysical parameters. We studied the contribution of these parameters to the predicted foF2 frequency. It was shown that the critical frequency of foF2 most strongly depends on 10.7 cm solar radiation and on the local solar time.
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