Length Analysis of Training Data for F10.7 Prediction Method Based on Deep Learning

DOI

10.2991/978-94-6463-040-4_167How to use a DOI?

Keywords

F10.7 index; atmospheric model; LSTM method

Abstract

The F10.7 solar radiation index is of great significance for the calculation of atmospheric density in low-Earth orbit. In recent years, a variety of neural network methods, especially the LSTM method, have been used for the modeling and forecasting of the F10.7 index, but the research on the length selection of historical data in the LSTM method is still very lacking. In this manuscript, the influence of different historical data lengths on the F10.7 index modeling and forecasting accuracy are studied in the F10.7 solar radiation index modeling and forecasting method based on LSTM. The reasonable selection interval of historical data length is given when applying LSTM method to forecast F10.7 index.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wenhui Cui
AU  - Xi Gan
AU  - Xiaofei Ma
AU  - Keshan He
AU  - Boru Wang
PY  - 2022
DA  - 2022/12/27
TI  - Length Analysis of Training Data for F10.7 Prediction Method Based on Deep Learning
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 1117
EP  - 1121
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_167
DO  - 10.2991/978-94-6463-040-4_167
ID  - Cui2022
ER  -