Space Weather Applications of Machine Intelligence (SWAMI) Seminar Series:
Machine Learning Efforts on Solar Flare Predictions by UoM Team
Hosted by: CU's Space Weather Technology, Research, and Education Center (SWx TREC)
Presented by: Yang Chen, University of Michigan
Thursday 10 September 2020, 11:30am – 12:30pm (MDT)
Click here for Zoom Seminar recording, Passcode: tG#pXo85
In this talk, we present our machine learning efforts, which show great promise towards early predictions of solar flare events. (1) We present a data pre-processing pipeline that is built to extract useful data from multiple sources -- Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and SDO/Atmospheric Imaging Assembly (AIA) -- to prepare inputs for machine learning algorithms. (2) For our strong/weak flare classification model, case studies show a significant increase in the prediction score around 20 hours before strong solar flare events, which implies that early precursors appear at least 20 hours prior to the peak of a flare event. (3) We develop a mixed Long Short Term Memory (LSTM) regression model to predict the maximum solar flare intensity within a 24-hour time window. (4) Our ongoing and future work will also be briefly mentioned.