摘要
Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our technosphere.The short-term solar eruptive activity prediction is an active field of research in the space weather prediction.Numerical,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive activities.With the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant progress.In this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.
基金
Science and Technology Facilities Council(STFC,Grant No.ST/M000826/1)
National Research Development and Innovation Office(OTKA,Grant No.K142987)Hungary for enabling this research
ST/S000518/1,PIA.CE.RI.2020-2022 Linea 2,CESAR 2020-35-HH.0,and UNKP-224-II-ELTE-186 grants
the support from ISSI-Beijing for their project“Step forward in solar flare and coronal mass ejection(CME)forecasting”
supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0560000)
the National Key R&D Program of China(Grant No.2021YFA1600504)
the National Natural Science Foundation of China(Grant No.11873060)。
