Flare Index Prediction with Machine Learning Algorithms
Author:
Funder
chinese academy of sciences
Publisher
Springer Science and Business Media LLC
Subject
Space and Planetary Science,Astronomy and Astrophysics
Link
https://link.springer.com/content/pdf/10.1007/s11207-021-01895-1.pdf
Reference70 articles.
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2. Baker, D.N., McPherron, R.L., Cayton, T.E., Klebesadel, R.W.: 1990, Linear prediction filter analysis of relativistic electron properties at $6.6~\text{R}_{E}$. J. Geophys. Res. 95(A9), 15133. DOI. ADS.
3. Benvenuto, F., Piana, M., Campi, C., Massone, A.M.: 2018, A hybrid supervised/unsupervised machine learning approach to solar flare prediction. Astrophys. J. 853(1), 90. DOI. ADS.
4. Bhattacharjee, S., Alshehhi, R., Dhuri, D.B., Hanasoge, S.M.: 2020, Supervised convolutional neural networks for classification of flaring and nonflaring active regions using line-of-sight magnetograms. Astrophys. J. 898, 98. DOI. ADS.
5. Bobra, M.G., Couvidat, S.: 2015, Solar flare prediction using SDO/HMI vector magnetic field data with a machine-learning algorithm. Astrophys. J. 798(2), 135. DOI. ADS.
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