Solar Flare Short-term Forecast Model Based on Long and Short-term Memory Neural Network

HE Xinran; ZHONG Qiuzhen; CUI Yanmei; LIU Siqing; SHI Yurong; YAN Xiaohui; WANG Zisiyu

doi:10.11728/cjss2022.05.210315028

Volume 42 Issue 5

Oct. 2022

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Article Navigation > Chinese Journal of Space Science > 2022 > 42(5): 862-872

HE Xinran, ZHONG Qiuzhen, CUI Yanmei, LIU Siqing, SHI Yurong, YAN Xiaohui, WANG Zisiyu. Solar Flare Short-term Forecast Model Based on Long and Short-term Memory Neural Network (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 862-872 doi: 10.11728/cjss2022.05.210315028

Citation:

HE Xinran, ZHONG Qiuzhen, CUI Yanmei, LIU Siqing, SHI Yurong, YAN Xiaohui, WANG Zisiyu. Solar Flare Short-term Forecast Model Based on Long and Short-term Memory Neural Network (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 862-872 doi: 10.11728/cjss2022.05.210315028

Citation:

HE Xinran, ZHONG Qiuzhen, CUI Yanmei, LIU Siqing, SHI Yurong, YAN Xiaohui, WANG Zisiyu. Solar Flare Short-term Forecast Model Based on Long and Short-term Memory Neural Network (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 862-872 doi: 10.11728/cjss2022.05.210315028

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Solar Flare Short-term Forecast Model Based on Long and Short-term Memory Neural Network

doi: 10.11728/cjss2022.05.210315028

HE Xinran^{1, 2, 3
,},
ZHONG Qiuzhen^{1, 2, 3
,
,},
CUI Yanmei^{1, 3},
LIU Siqing^{1, 2, 3},
SHI Yurong^{1, 2, 3},
YAN Xiaohui^{1, 2, 3},
WANG Zisiyu^{1, 2, 3}

1.
National Space Science Center, Chinese Academy of Sciences, Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
Key Laboratory of Science and Technology on Environmental Space Situation Awareness, Chinese Academy of Sciences, Beijing 100190

Received Date: 2021-03-12
Accepted Date: 2021-05-19
Rev Recd Date: 2021-05-17

Available Online: 2022-09-22

Abstract

Abstract

Solar flares are a kind of violent solar eruptive activity phenomenon and an important warning device of space weather disturbance. In space weather forecasting, flare forecasting is an important forecast content. This paper proposes a flare prediction model based on long and short-term memory neural network, which uses the time sequence of magnetic field changes in the solar active area in the past 24 h to construct samples, and analyzes the time series evolution of magnetic field characteristics through the long and short-term memory neural network to predict whether ≥M-level flares will occur in the next 48 h. This paper uses a data set for all active area samples from May 2010 to May 2017, and selects 10 magnetic field characteristic parameters of SDO/HMI SHARP. In the modeling process, six feature parameters with high weight, gain rate and coverage rate were selected as input parameters through XGBoost method. Through test comparison, the false report rate and accuracy rate of the model are similar to the traditional machine learning model, and the accuracy rate and critical success index are better than the traditional machine learning model, which are 0.7483 and 0.7402 respectively. The overall effect of the model is better than that of the traditional machine learning model.
- Solar flare prediction,
- Deep learning,
- Long and short-term memory neural network

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References

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Solar Flare Short-term Forecast Model Based on Long and Short-term Memory Neural Network

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