Interpretable ML-based forecasting of CMEs associated with flares

Abstract

Coronal Mass Ejections (CMEs) that cause geomagnetic disturbances at Earth can be found in conjunction with flares, filament eruptions, or independent. Though, flares and CMEs are understood as triggered by a common physical process magnetic reconnection, yet, the degree of association is challenging to predict. Using eight Machine Learning models, we attempted to predict the association of CMEs with flares. From Solar Dynamic Observatory/Helioseismicand Magnetic Imager (SDO/HMI) images, magnetic field features known as Space Weather HMI Active Region Patches (SHARPs) are derived and utilised as numerical input to ML models. Since flares with CME events are occasional, to address the class imbalance, we have explored approaches such as undersampling majority class, oversampling minority class and Synthetic Minority Oversampling TEchnique (SMOTE) on the training data. We observe that the SVM and LDA performs best among all with True Skill Score (TSS) around 0.78±0.09 and 0.8±0.05 respectively. To improve the predictions, we attempted to incorporate differences in features from the prior time lag data as temporal information along side the existing dataset. LDA achieves a TSS of 0.92±0.04. We study important SHARP features that are essential for formulating predictions of SVM and LDA models using the wrapper technique and permutation based model interpretation methods for both the approaches. The study will help in better understanding of the physical processes as well in developing real time prediction of the CME events.