NSSC OpenIR
Solar Flare Predictive Features Derived from Polarity Inversion Line Masks in Active Regions Using an Unsupervised Machine Learning Algorithm
Alternative TitleWOS:000525489700001
Wang, Jingling1; Zhang, Yuhang2; Webber, Shea A. Hess; Liu, Siqing1,4; Meng, Xuejie1; Wang, Tieyan5
Source PublicationASTROPHYSICAL JOURNAL
2020
Volume892Issue:2Pages:140
DOI10.3847/1538-4357/ab7b6c
ISSN0004-637X
Language英语
KeywordSolar flares Space weather Classification Solar active regions MAGNETIC-FIELD PROPERTIES PRODUCTIVITY NONPOTENTIALITY
AbstractThe properties of the polarity inversion line (PIL) in solar active regions (ARs) are strongly correlated to flare occurrences. The PIL mask, enclosing the PIL areas, has shown significant potential for improving machine-learning-based flare prediction models. In this study, an unsupervised machine-learning algorithm, Kernel Principle Component Analysis (KPCA), is adopted to directly derive features from the PIL mask and difference PIL mask, and use those features to classify ARs into two categories-non-strong flaring ARs and strong-flaring (M-class and above flares) ARs-for time-in-advance from one hour to 72 hr at a 1 hr cadence. The two best features are selected from the KPCA results to develop random-forest classifiers for predicting flares, and the models are then evaluated and compared to similar models based on the R value and difference R value. The results show that the features derived from the PIL masks by KPCA are effective in predicting flare occurrence, with overall better Fisher ranking scores and similar predictive statistics as the R value characteristics.
Indexed BySCI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7620
Collection中国科学院国家空间科学中心
Affiliation1.Chinese Acad Sci, Natl Space Sci Ctr, Beijing, Peoples R China
2.Chinese Acad Sci, Key Lab Sci & Technol Environm Space Situat Aware, Beijing, Peoples R China
3.Xidian Univ, Sch Comp Sci & Technol, Xian 710126, Peoples R China
4.Stanford Univ, WW Hansen Expt Phys Lab, Stanford, CA 94305 USA
5.Univ Chinese Acad Sci, Beijing, Peoples R China
6.Rutherford Appleton Lab, RAL Space, Harwell Oxford, Didcot OX11 0QX, Oxon, England
Recommended Citation
GB/T 7714
Wang, Jingling,Zhang, Yuhang,Webber, Shea A. Hess,et al. Solar Flare Predictive Features Derived from Polarity Inversion Line Masks in Active Regions Using an Unsupervised Machine Learning Algorithm[J]. ASTROPHYSICAL JOURNAL,2020,892(2):140.
APA Wang, Jingling,Zhang, Yuhang,Webber, Shea A. Hess,Liu, Siqing,Meng, Xuejie,&Wang, Tieyan.(2020).Solar Flare Predictive Features Derived from Polarity Inversion Line Masks in Active Regions Using an Unsupervised Machine Learning Algorithm.ASTROPHYSICAL JOURNAL,892(2),140.
MLA Wang, Jingling,et al."Solar Flare Predictive Features Derived from Polarity Inversion Line Masks in Active Regions Using an Unsupervised Machine Learning Algorithm".ASTROPHYSICAL JOURNAL 892.2(2020):140.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Jingling]'s Articles
[Zhang, Yuhang]'s Articles
[Webber, Shea A. Hess]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Jingling]'s Articles
[Zhang, Yuhang]'s Articles
[Webber, Shea A. Hess]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Jingling]'s Articles
[Zhang, Yuhang]'s Articles
[Webber, Shea A. Hess]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.