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Flare Index Prediction with Machine Learning Algorithms
Chen, Anqin1; Ye, Qian1; Wang, Jingxiu2,3
2021-10-01
Source PublicationSOLAR PHYSICS
ISSN0038-0938
Volume296Issue:10Pages:17
AbstractSolar flares are one of the most important sources of disastrous space weather events, leading to negative effects on spacecrafts and living organisms. It is very important to predict solar flares to minimize the potential losses. In this paper, we use three different machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF), and XGBoost (XGB) to predict the total flare index T-flare and the maximum flare index M-flare of an active region (AR) within the subsequent of 24, 48, and 72 hrs. First, we selected 54514 vector magnetograms of 129 ARs on the visible solar hemisphere in solar cycle 24 whose maximum sunspot groups' area was larger than 400 mu h. Then the following four magnetic parameters of each magnetogram were calculated: 1) the total magnetic flux |Phi(tot)|, 2) the total photospheric free magnetic energy density E-free, 3) the gradient-weighted integral length of the neutral line with horizontal magnetic gradient of line-of-sight magnetic field larger than 0.1 Gkm(-1) (WLSG), and 4) the area with magnetic shear angle larger than 40 degrees (A(Psi)), as well as T-flare and M-flare corresponding to each magnetogram. Afterward, we split samples randomly into training (85% of the whole data) and testing (15%) data sets. After hyperparameter tuning and model construction we found that RF is an optimal algorithm for the prediction task and that the coefficients of determination (R-2) of test data set via the majority of RF models are beyond 0.97. In addition, the feature importance of RF and XGB models indicates that |Phi(tot) | and E-free are two optimal parameters to predict both T-flare and M-flare, and |Phi(tot)| and E-free are the best parameters for M-flare and T-flare, respectively.
KeywordActive regions magnetic fields Flares Forecasting Machine learning
Funding OrganizationStrategic Priority Program on Space Science, Chinese Academy of Sciences ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Key Research Program of Frontier Sciences CAS ; Key Research Program of Frontier Sciences CAS ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Key Research Program of Frontier Sciences CAS ; Key Research Program of Frontier Sciences CAS ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Key Research Program of Frontier Sciences CAS ; Key Research Program of Frontier Sciences CAS ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Key Research Program of Frontier Sciences CAS ; Key Research Program of Frontier Sciences CAS
DOI10.1007/s11207-021-01895-1
WOS KeywordSUPER-ACTIVE REGIONS ; MAGNETIC-FIELD PROPERTIES ; SOLAR-FLARES ; NEURAL-NETWORK ; EVOLUTION ; SHEAR ; FLUX ; EMERGENCE ; MODEL
Language英语
Funding ProjectStrategic Priority Program on Space Science, Chinese Academy of Sciences[XDA15350203] ; Key Research Program of Frontier Sciences CAS[ZDBS-LY-SLH013]
Funding OrganizationStrategic Priority Program on Space Science, Chinese Academy of Sciences ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Key Research Program of Frontier Sciences CAS ; Key Research Program of Frontier Sciences CAS ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Key Research Program of Frontier Sciences CAS ; Key Research Program of Frontier Sciences CAS ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Key Research Program of Frontier Sciences CAS ; Key Research Program of Frontier Sciences CAS ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Strategic Priority Program on Space Science, Chinese Academy of Sciences ; Key Research Program of Frontier Sciences CAS ; Key Research Program of Frontier Sciences CAS
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000708887500001
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/74927
Collection中国科学院国家天文台
Corresponding AuthorYe, Qian
Affiliation1.China Meteorol Adm, Natl Ctr Space Weather, Key Lab Space Weather, Beijing 100081, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Natl Astron Observatories, Key Lab Solar Act, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Chen, Anqin,Ye, Qian,Wang, Jingxiu. Flare Index Prediction with Machine Learning Algorithms[J]. SOLAR PHYSICS,2021,296(10):17.
APA Chen, Anqin,Ye, Qian,&Wang, Jingxiu.(2021).Flare Index Prediction with Machine Learning Algorithms.SOLAR PHYSICS,296(10),17.
MLA Chen, Anqin,et al."Flare Index Prediction with Machine Learning Algorithms".SOLAR PHYSICS 296.10(2021):17.
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