The influence of active region information on the prediction of solar flares: an empirical model using data mining
Predicting the occurrence of solar flares is a challenge of great importance for many space weather scientists and users. We introduce a data mining approach, called Behavior Pattern Learning (BPL), for automatically discovering correlations between solar flares and active region data, in order...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2005-11-01
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| Series: | Annales Geophysicae |
| Online Access: | https://www.ann-geophys.net/23/3129/2005/angeo-23-3129-2005.pdf |
| Summary: | Predicting the occurrence of solar flares is a challenge of great importance
for many space weather scientists and users. We introduce a data mining
approach, called Behavior Pattern Learning (BPL), for automatically
discovering correlations between solar flares and active region data, in
order to predict the former. The goal of BPL is to predict the interval of
time to the next solar flare and provide a confidence value for the associated
prediction. The discovered correlations are described in terms of
easy-to-read rules. The results indicate that active region dynamics is
essential for predicting solar flares. |
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| ISSN: | 0992-7689 1432-0576 |