![]() The major results of this study are (1) we successfully implemented an efficient and effective full-disk flare predictor for operational forecasting using compressed images of solar magnetograms (2) Our candidate model for multi-class flare prediction achieves an average TSS of 0.36 and average HSS of 0.31. ![]() ![]() We trained our models using data-augmented oversampling to address the existing class-imbalance issue by following a time-segmented cross-validation strategy to effectively understand the accuracy performance of our models and used true skill statistics (TSS) and Heidke skill score (HSS) as metrics to compare and evaluate. For this, we collect compressed 8-bit images derived from full-disk line-of-sight magnetograms provided by the Helioseismic and Magnetic Imager (HMI) instrument onboard Solar Dynamics Observatory (SDO). We perform our experiments in all three modes using three well-known pretrained CNN models-AlexNet, VGG16 and ResNet34. We selected three prediction modes, among which two are binary for predicting the occurrence of \(\ge \)M1.0 and \(\ge \)C4.0 class flares and one is a multi-class mode for predicting the occurrence of ![]()
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