ED‐AttConvLSTM: An Ionospheric TEC Map Prediction Model Using Adaptive Weighted Spatiotemporal Features

In this paper, we propose a novel Total Electron Content (TEC) map prediction model, named ED‐AttConvLSTM, using a Convolutional Long Short‐Term Memory (ConvLSTM) network and attention mechanism based on encoder‐decoder structure. The inclusion of the attention mechanism enhances the efficient utili...

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Veröffentlicht in:	Space Weather Jg. 22; H. 3
Hauptverfasser:	Li, Liangchao, Liu, Haijun, Le, Huijun, Yuan, Jing, Wang, Haoran, Chen, Yi, Shan, Weifeng, Ma, Li, Cui, Chunjie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Washington John Wiley & Sons, Inc 01.03.2024 Wiley
Schlagworte:	Accuracy Deep learning Feature extraction General circulation models Geomagnetic storms Geophysics Ionosphere Magnetic storms Performance evaluation Prediction models Predictions Solar activity Time series Total Electron Content
ISSN:	1542-7390, 1539-4964, 1542-7390
Online-Zugang:	Volltext
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