NSSC OpenIR
Machine Learning Approach for Solar Wind Categorization
Alternative TitleWOS:000537134000009
Li, Hui1; Wang, Chi2; Tu, Cui3,4; Xu, Fei5
Source PublicationEARTH AND SPACE SCIENCE
2020
Volume7Issue:5Pages:UNSP e2019EA000997
DOI10.1029/2019EA000997
Language英语
Keywordsolar wind classification machine learning automatical method k-nearest neighbors space weather early warning MAGNETIC-FIELD SIGNATURES CORONAL MASS EJECTIONS HIGH-SPEED STREAMS HELIUM CLASSIFICATION CLOUDS DRIVEN CYCLE
AbstractSolar wind classification is conducive to understanding the ongoing physical processes at the Sun and in solar wind evolution in interplanetary space, and, furthermore, it is helpful for early warning of space weather events. With rapid developments in the field of artificial intelligence, machine learning approaches are increasingly being used for pattern recognition. In this study, an approach from machine learning perspectives is developed to automatically classify the solar wind at 1 AU into four types: coronal-hole-origin plasma, streamer-belt-origin plasma, sector-reversal-region plasma, and ejecta. By exhaustive enumeration, an eight-dimensional scheme (B-T, N-P, T-P, V-P, N-alpha p, T-exp/T-P, S-p, and M-f) is found to perform the best among 8,191 combinations of 13 solar wind parameters. Ten popular supervised machine learning models, namely, k-nearest neighbors (KNN), Support Vector Machines with linear and radial basic function kernels, Decision Tree, Random Forest, Adaptive Boosting, Neural Network, Gaussian Naive Bayes, Quadratic Discriminant Analysis, and eXtreme Gradient Boosting, are applied to the labeled solar wind data sets. Among them, KNN classifier obtains the highest overall classification accuracy, 92.8%. Although the accuracy can be improved by 1.5% when O7+/O6+ information is additionally considered, our scheme without composition measurements is still good enough for solar wind classification. In addition, two application examples indicate that solar wind classification is helpful for the risk evaluation of predicted magnetic storms and surface charging of geosynchronous spacecraft.
Indexed BySCI
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7573
Collection中国科学院国家空间科学中心
Affiliation1.Chinese Acad Sci, Natl Space Sci Ctr, State Key Lab Space Weather 3, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Natl Space Sci Ctr, Lab Near Space Environm, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Coll Mat Sci & Optoelect Technol, Beijing, Peoples R China
6.Nanjing Univ Informat Sci & Technol, Phys Dept, Nanjing, Peoples R China
Recommended Citation
GB/T 7714
Li, Hui,Wang, Chi,Tu, Cui,et al. Machine Learning Approach for Solar Wind Categorization[J]. EARTH AND SPACE SCIENCE,2020,7(5):UNSP e2019EA000997.
APA Li, Hui,Wang, Chi,Tu, Cui,&Xu, Fei.(2020).Machine Learning Approach for Solar Wind Categorization.EARTH AND SPACE SCIENCE,7(5),UNSP e2019EA000997.
MLA Li, Hui,et al."Machine Learning Approach for Solar Wind Categorization".EARTH AND SPACE SCIENCE 7.5(2020):UNSP e2019EA000997.
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