Solar Flare Forecasting with Deep Neural Networks using Compressed Full-disk HMI Magnetograms

Prediction of solar flares is a challenging problem in space weather forecasting that has piqued the interest of many researchers in recent years due to improved data availability and the advancements in the field of machine learning and deep learning. In this paper, we present a solution to full-disk flare prediction using compressed magnetogram images, which was performed by training a set of Convolutional Neural Networks to perform operations-ready flare forecasts. W e s elected two prediction modes, which are both binary for predicting the occurrence of ≥M1.0 and ≥C1.0 class flares within the next 24 hours. For this, we use a simple yet powerful pre-trained AlexNet model and we collect compressed images derived from solar magnetograms provided by the Helioseismic and Magnetic Imager (HMI) instrument onboard Solar Dynamics Observatory (SDO). We followed two time-segmented cross-validation strategies: chronological and non-chronological, to effectively understand the predictive skill of our models. We also trained our models using data-augmentation and oversampling to address the existing class-imbalance issue and used true skill statistic (TSS) and Heidke skill score (HSS) as metrics to compare and evaluate. The major results of this study are (1) we successfully implemented an efficient and effective full-disk flare predictor ready for operational forecasting using 8-bit compressed images of solar magnetograms without further preprocessing; (2) Our candidate model achieves an average TSS of 0.47±0.06 for ≥M1.0 mode and 0.63±0.05 for ≥C1.0 mode, and HSS of 0.35±0.05 for ≥M1.0 and 0.62±0.05 for ≥C1.0 mode. Our experimental evaluation also suggests that training a flare prediction model is heavily influenced by the sampling strategies involved due to the imbalanced nature of the datasets and predicting ≥M1.0 class flares is a more challenging task compared to ≥C1.0 ones.