Short-term forecasting of spread F using LSTM network

SUN Shuji; XU Tong; BAN Panpan; HU Ranran; CHEN Chun

doi:10.12265/j.cjors.2023042

Volume 38 Issue 4

Sep. 2023

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CHINESE JOURNAL OF RADIO SCIENCE > 2023 > 38(4): 686-690, 720. > DOI: 10.12265/j.cjors.2023042

SUN S J, XU T, BAN P P, et al. Short-term forecasting of spread F using LSTM network[J]. Chinese journal of radio science，2023，38（4）：686-690 + 720. （in Chinese）. DOI: 10.12265/j.cjors.2023042

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Short-term forecasting of spread F using LSTM network

SUN Shuji^1,,
XU Tong^1,,
BAN Panpan^{1, 2, ,},
HU Ranran¹,
CHEN Chun¹

1.
China Research Institute of Radiowave Propagation, Qingdao 266107, China
2.
Xidian University, Xi’an 717071, China

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Received Date: February 13, 2023
Accepted Date: July 03, 2023
Available Online: July 03, 2023

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Abstract

Abstract

Considering the main mechanism of spread F and the available data related to the occurrence of spread F in our country, a model based on long short-term memory (LSTM) network is built to forecast spread F in 3 hours. The models for Manzhouli, Beijing, and Haikou were built and tested using data in 2015 and 2016. The mean accuracy rates in Manzhouli, Beijing and Haiou are 92.4%, 95.3%, and 96.0%. The mean precision rates are 75.0%, 61.2%, and 62.6%, and the mean recall rates are 73.0%, 50.6%, and 31.5%, respectively. It is shown that the model has a high prediction ability in most cases, but it still needs to improve the ability of the model in some cases, especially in low latitude like Haikou station with a lower recall rate.
- deep learning,
- spread F,
- ionosphere,
- long short-term memory (LSTM) network,
- forecasting model

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References

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