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Solar Flare Forecasting with Machine Learning and Novel Statistical Models

Presentation #108.01 in the session Improving Understanding of the Sun-Earth System Through Advanced Statistical and Machine Learning Techniques.

Published onOct 20, 2022
Solar Flare Forecasting with Machine Learning and Novel Statistical Models

We present novel statistical methods towards early forecasting of solar flare events, and compare them with machine learning approaches that we have adopted in our previous work. The data sources that we use include: Geostationary Operational Environmental Satellites (GOES), Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and SDO/Atmospheric Imaging Assembly (AIA). The results that I will show in the talk include: (1) strong and weak flare classification with spatial statistics features, together with SHARP and topological parameters; (2) active region based solar flare intensity prediction with mixed LSTM regression; and (3) additive model for solar flare forecasting combining data of various types and sources (SHARP parameters, HMI and AIA images).

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