Research on Solar Proton Event in the 23rd Solar Cycle Using the Machine Learning Methods

Abstract

Solar Proton Event (SPE) can pose crucial risks to the spacecraft. It is meaningful to analyze and build the relationships between SPE and the associated Coronal Mass Ejection (CME) and solar flares. In this study, the SPE in the 23rd solar cycle is investigated by using machine learning methods. Datasets were constructed based on CME and the solar proton events lists from 1997 to 2006 from the CDA web database. Apriori algorithm are used to survey the correlations between SPEs and the characteristics of flares and CME. The results show that X class flares, full halo CME, high speed (greater than 1000 km·s^-1) CME, and western flares are the four characteristics that most likely to be associated with SPE. The corresponding probabilities are 0.366, 0.355, 0.30 and 0.155. The SPE probabilities at the condition of more than one (CME or flare) features occurring simultaneously were exhibited as well. Using the over sampled CME and flares features, five SPE prediction models are built through five different supervised machine learning algorithms, thus Logistic Regression, Support Vector Classification, <italic>K</italic>-nearest neighbor, Random Forest and Gradient Boosting Decision Tree. The models all present pretty good prediction accuracy (>0.94), precision (>0.96) and recall rate (>0.91).