基于机器学习方法的短期太阳爆发活动预报模型: 综述 [Short-term solar burst activity prediction models based on machine learning methods: A review]

Solar burst activities mainly include solar flares, coronal mass ejections (CMEs) and solar proton events (SPEs). Solar burst activities have important impacts on space weather and high technology. Short-term forecasting of solar burst activities is an active research area in space weather forecasting. Currently, numerical, statistical and machine learning methods are used to establish solar burst activity forecast models. With the development of space-based and ground-based observation equipment, a large amount of solar observation data has been accumulated, and data-driven solar burst activity forecast models have made significant progress. This paper introduces the application of machine learning algorithms in solar burst activity forecasting, summarizes the forecast modeling process, outlines the progress of solar burst activity forecast models, and looks forward to possible future research directions.