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Found 3 papers, 2 papers with code
Incorporating Polar Field Data for Improved Solar Flare Prediction
In this paper, we consider incorporating data associated with the sun's north and south polar field strengths to improve solar flare prediction performance using machine learning models.
Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data
We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours.
Solar Flare Intensity Prediction with Machine Learning Models
We develop a mixed Long Short Term Memory (LSTM) regression model to predict the maximum solar flare intensity within a 24-hour time window 0$\sim$24, 6$\sim$30, 12$\sim$36 and 24$\sim$48 hours ahead of time using 6, 12, 24 and 48 hours of data (predictors) for each Helioseismic and Magnetic Imager (HMI) Active Region Patch (HARP).
Solar and Stellar Astrophysics
