The Bulletin of The Korean Astronomical Society (천문학회보)
- Volume 37 Issue 2
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- Pages.123.1-123.1
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- 2012
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- 1226-2692(pISSN)
Near-real time Kp forecasting methods based on neural network and support vector machine
- Ji, Eun-Young (Astronomy and Space Science, Kyung Hee University) ;
- Moon, Yong-Jae (School of Space Research, Kyung Hee University) ;
- Park, Jongyeob (School of Space Research, Kyung Hee University) ;
- Lee, Dong-Hun (School of Space Research, Kyung Hee University)
- Published : 2012.10.17
Abstract
We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms (
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