Solar Flare Intensity Prediction With Machine Learning Models. Issue 7 (10th July 2020) - Jiao, Zhenbang; Sun, Hu; Wang, Xiantong; Manchester, Ward; Gombosi, Tamas; Hero, Alfred; Chen, Yang
Abstract: We develop a mixed long short‐term memory (LSTM) regression model to predict the maximum solar flare intensity within a 24‐hr time window 0–24, 6–30, 12–36, and 24–48 hr ahead of time using 6, 12, 24, and 48 hr of data (predictors) for each Helioseismic and Magnetic Imager (HMI) Active Region Patch (HARP). The model makes use of (1) the Space‐Weather HMI Active Region Patch (SHARP) parameters as predictors and (2) the exact flare intensities instead of class labels recorded in the Geostationary Operational Environmental Satellites (GOES) data set, which serves as the source of the response variables. Compared to solar flare classification, the model offers us more detailed information about the exact maximum flux level, that is, intensity, for each occurrence of a flare. We also consider classification models built on top of the regression model and obtain better results in solar flare classifications as compared to Chen et al. (2019, https://doi.org/10.1029/2019SW002214 ). Our results suggest that the most efficient time period for predicting the solar activity is within 24 hr before the prediction time using the SHARP parameters and the LSTM model. Key Points: We develop deep learning models to predict solar flare intensity values instead of flare classes from SHARP parameters in SDO/HMI data set directly We use time‐series information from both flaring time and nonflaring time in our model As opposed to solar flare classification, directly predicting solar flare intensity gives more detailed information about every occurrence of flares of each class