Neural network-based nonlinear prediction of magnetic storms

Details Neural network-based nonlinear prediction of magnetic storms Neural network-based nonlinear prediction of magnetic storms

Mar 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002jastp..64..651j&link_type=abstract

Journal of Atmospheric and Solar-Terrestrial Physics, Volume 64, Issue 5-6, p. 651-656.

Physics

4

Scientific paper

The best known manifestations of the solar wind impact on the Earth's magnetosphere are the geomagnetic storms. At middle latitudes, the magnetic storm is best described in terms of the horizontal component of the geomagnetic field. We use the method of neural networks which is suitable for nonlinear dynamical systems modeling and for the nowcasting as well as forecasting of the magnetic storms. For constructing a neural network model, a multivariate method for determination of the inputs is applied first. This method enables us to reduce the original 17 input variables to two variables, the so-called principal components. The performance of the model is characterized by the correlation coefficient /ρ. Its mean value is 0.93 considering two principal components and time history 6h. Our interest is also focused on the more than 1h ahead forecasting of the geomagnetic index Dst. Another question investigated here is how the inclusion of the history of Dst index into the input matrix influences the predicting ability of the network.

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