Two-Stage Solar Flare Forecasting Based on Convolutional Neural Networks

Author:

Chen Jun1ORCID,Li Weifu12,Li Shuxin345,Chen Hong1ORCID,Zhao Xuebin1,Peng Jiangtao2,Chen Yanhong34,Deng Hao1

Affiliation:

1. College of Science, Huazhong Agricultural University, Wuhan 430070, China

2. Hubei Key Laboratory of Applied Mathematics, Hubei University, Wuhan 430062, China

3. Nation Space Science Center, Chinese Academy of Science, Beijing 100190, China

4. Key Laboratory of Science and Technology on Environment Space Situation Awareness, Beijing 100190China

5. University of Chinese Academy of Science, Beijing 100049, China

Abstract

Solar flares are solar storm events driven by the magnetic field in the solar activity area. Solar flare, often associated with solar proton event or CME, has a negative impact on ratio communication, aviation, and aerospace. Therefore, its forecasting has attracted much attention from the academic community. Due to the limitation of the unbalanced distribution of the observation data, most techniques failed to effectively learn complex magnetic field characteristics, leading to poor forecasting performance. Through the statistical analysis of solar flare magnetic map data observed by SDO/HMI from 2010 to 2019, we find that unsupervised clustering algorithms have high accuracy in identifying the sunspot group in which the positive samples account for the majority. Furthermore, for these identified sunspot groups, the ensemble model that integrates the capability of boosting and convolutional neural network (CNN) achieves high-precision prediction of whether the solar flares will occur in the next 48 hours. Based on the above findings, a two-stage solar flare early warning system is established in this paper. The F1 score of our method is 0.5639, which shows that it is superior to the traditional methods such as logistic regression and support vector machine (SVM).

Funder

Natural Science Foundation of Hubei Province

Hubei Key Laboratory of Applied Mathematics

National Natural Science Foundation of China

Publisher

American Association for the Advancement of Science (AAAS)

Subject

General Medicine

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