Prediction of Ionospheric Total Electron Content Based on Causal Convolutional and LSTM Network

Author:

TANG Siyu,HUANG Zhi

Publisher

National Space Science Center, Chinese Academy of Sciences

Subject

General Medicine

Reference18 articles.

1. KERSLEY L, MALAN D, PRYSE S E, et al. Total electron content-A key parameter in propagation: measurement and use in ionospheric imaging[J]. Annals of Geophysics, 2004, 47(2/3): 1067-1091

2. WINGLEE R M, CHUA D, BRITTNACHER M, et al. Global impact of ionospheric outflows on the dynamics of the magnetosphere and cross-polar cap potential[J]. Journal of Geophysical Research: Space Physics, 2002, 107(A9): 1237

3. ACHARYA R, ROY B, SIVARAMAN M R, et al. Prediction of ionospheric total electron content using adaptive neural network with in-situ learning algorithm[J]. Advances in Space Research, 2011, 47(1): 115-123

4. BILITZA D, ALTADILL D, ZHANG Y L, et al. The International Reference Ionosphere 2012-a model of international collaboration[J]. Journal of Space Weather and Space Climate, 2014, 4: A07

5. ZHANG Xiaohong, REN Xiaodong, WU Fengbo, et al. Short-term TEC prediction of ionosphere based on ARIMA model[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(2): 118-124
张小红, 任晓东, 吴风波, 等. 自回归移动平均模型的电离层总电子含量短期预报[J]. 测绘学报, 2014, 43(2): 118-124

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