Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

Forecasting the Ionospheric f0F2 Parameter One Hour in Advance Using Recurrent Neural Network

Authors
Zhe Lv, Changjun Yu, Aijun Liu
Corresponding Author
Zhe Lv
Available Online May 2019.
DOI
10.2991/cnci-19.2019.37How to use a DOI?
Keywords
Ionospheric critical frequency, modeling and forecasting, recurrent neural networks
Abstract

It is difficult to forecast the state of ionosphere because the time-varying characteristics. Using recurrent neural network(RNN) one hour ahead prediction of the critical parameter of ionospheric F2 layer(f0F2) is realized. The prediction model is developed based on 11 years (from 2005 to 2016) of data measured from ionospheric vertical stations in China. By analyzing time series correlation of f0F2 and solar-terrestrial and geomagnetic activities, several parameters are selected as inputs. Though training the RNN model, the forecasting values one hour ahead can be obtained. For this time-series problem, the predicted ability of RNN is better than Artificial Neural Network(ANN) and the autocorrelation method by comparing the results of three different algorithms.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)
Series
Advances in Computer Science Research
Publication Date
May 2019
ISBN
10.2991/cnci-19.2019.37
ISSN
2352-538X
DOI
10.2991/cnci-19.2019.37How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhe Lv
AU  - Changjun Yu
AU  - Aijun Liu
PY  - 2019/05
DA  - 2019/05
TI  - Forecasting the Ionospheric f0F2 Parameter One Hour in Advance Using Recurrent Neural Network
BT  - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)
PB  - Atlantis Press
SP  - 249
EP  - 258
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnci-19.2019.37
DO  - 10.2991/cnci-19.2019.37
ID  - Lv2019/05
ER  -