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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:GPS solutions Jg. 27; H. 2; S. 95
Hauptverfasser: de Paulo, M. C. M., Marques, H. A., Feitosa, R. Q., Ferreira, M. P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023
Springer Nature B.V
Schlagworte:
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-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 forecast VTEC based on global ionosphere maps (GIMs) of the days before the prediction period. We proposed modifications to the encoder–decoder convolutional long short-term memory (ED-ConvLSTM) architecture by innovatively using GIMs from several days and exploring the previous day’s GIM. Three new architectures were tested: The first one uses a residual connection to force the network to learn the difference between the previous and the next-day GIM; the second one uses the memory from the encoder to improve the transformation from the previous to the next-day GIM; and a third one uses 3 × 3 kernels on the encoder and 1 × 1 kernels on the decoder. Two experiments were performed using the international GNSS service (IGS) final GIM product from the years 2014–2015 (high solar activity) and 2019–2020 (low solar activity). The proposed architectures obtained better results than the original version of ED-ConvLSTM and other baseline models. We evaluated the influence of the number of GIMs (from one to four days) in the next-day GIM predictions. The results suggest that providing more than one day of GIM to the proposed networks can lead to better prediction metrics.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1080-5370
1521-1886
DOI:10.1007/s10291-023-01442-4