New encoder–decoder convolutional LSTM neural network architectures for next-day global ionosphere maps forecast

Global navigation satellite system (GNSS) signals are significantly affected by the ionosphere. An efficient way to assess the ionospheric effects on GNSS signals is by retrieving the vertical total electron content (VTEC). Here, we propose convolutional recurrent neural network architectures to for...

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Bibliographic Details
Published in:	GPS solutions Vol. 27; no. 2; p. 95
Main Authors:	de Paulo, M. C. M., Marques, H. A., Feitosa, R. Q., Ferreira, M. P.
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023 Springer Nature B.V
Subjects:	Atmospheric Sciences Automotive Engineering Coders Computer architecture Earth and Environmental Science Earth Sciences Electrical Engineering Geophysics/Geodesy Global navigation satellite system Ionosphere Kernels Neural networks Recurrent neural networks Solar activity Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Deep learning Ionosphere ConvLSTM Forecast Total Electron Content Residual neural networks
ISSN:	1080-5370, 1521-1886
Online Access:	Get full text
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