Computer Science – Learning
Scientific paper
Nov 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008adspr..42.1450h&link_type=abstract
Advances in Space Research, Volume 42, Issue 9, p. 1450-1456.
Computer Science
Learning
2
Scientific paper
Solar activity prediction services started in 1960’s in National Astronomical Observatories, Chinese Academy of Sciences (NAOC). As one of the members of the International Space Environment Service (ISES), Regional Warning Center of China (RWC-China) was set up in 1990’s. Solar Activity Prediction Center (SAPC), as one of the four sub-centers of RWC-China, is located in NAOC. Solar activity prediction studies and services in NAOC cover short-term, medium-term, and long-term forecast of solar activities. Nowadays, certain prediction models, such as solar X-ray flare model, solar proton event model, solar 10 cm radio flux model, have been established for the practical prediction services. Recently, more and more physical analyses are introduced in the studies of solar activity prediction, such as the magnetic properties of solar active regions and magnetic structure of solar atmosphere. Besides traditional statistics algorithms, Machine Learning and Artificial Intelligence techniques, such as Support Vector Machine (SVM) method, are employed in the establishment of forecast models. A Web-based integrated platform for solar activity data sharing and forecast distribution is under construction.
Cui Yanmei
Du Zhanle
He Han
He Yulin
Li Rong
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