Forecasting solar energetic particles using multi-source data from solar flares, CMEs, and radio bursts with machine learning approaches
摘要
This study presents a consistent method to the inherently imbalanced problem of predicting solar energetic particle (SEP) events, using a variety of datasets that include solar flares, coronal mass ejections (CMEs), and radio bursts. We applied several machine learning (ML) methods, including Random Forests (RF), Decision Trees (dtree), and Support Vector Machines (SVM) with both linear (linSVM) and nonlinear (svm) kernels. To assess model performance, we used standard metrics such as Probability of Detection (POD), False Alarm Rate (FAR), True Skill Statistic (TSS), and Heidke Skill Score (HSS). Our results show that the RF model consistently outperforms the other algorithms across datasets containing flares, CMEs, and radio bursts. For the sweep frequency dataset, RF achieved a POD of