| Issue |
E3S Web Conf.
Volume 196, 2020
XI International Conference “Solar-Terrestrial Relations and Physics of Earthquake Precursors”
|
|
|---|---|---|
| Article Number | 02007 | |
| Number of page(s) | 6 | |
| Section | Geophysical Fields and their Interactions | |
| DOI | https://doi.org/10.1051/e3sconf/202019602007 | |
| Published online | 16 October 2020 | |
Application of deep learning methods to predict ionosphere parameters in real time
Institute of Cosmophysical Research and Radio Wave Propagation of the Far Eastern Branch of Russian Academy of Science, 684034, Kamchatskiy kray, Paratunka, Mirnaya str. 7, Russia
* Corresponding author: vmochalov@ikir.ru
In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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