Prediction of the geomagnetic storm associated Dstindex using an artificial neural network algorithm

Details Prediction of the geomagnetic storm associated Dstindex using an artificial neural network algorithm Prediction of the geomagnetic storm associated Dstindex using an artificial neural network algorithm

Apr 1999

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999ep%26s...51..307k&link_type=abstract

Earth, Planets and Space, Volume 51, p. 307-313.

Computer Science

Learning

5

Scientific paper

In order to enhance the reproduction of the recovery phase Dstindex of a geomagnetic storm which has been shown by previous studies to be poorly reproduced when compared with the initial and main phases, an artificial neural network with one hidden layer and error back-propagation learning has been developed. Three hourly Dstvalues before the minimum Dstin the main phase in addition to solar wind data of IMF southward-component Bs, the total strength Bt and the square root of the dynamic pressure, √(n v2), for the minimum Dst, i.e., information on the main phase was used to train the network. Twenty carefully selected storms from 1972-1982 were used for the training, and the performance of the trained network was then tested with three storms of different Dststrengths outside the training data set. Extremely good agreement between the measured Dst and the modeled Dsthas been obtained for the recovery phase. The correlation coefficient between the predicted and observed Dst is more than 0.95. The average relative variance is 0.1 or less, which means that more than 90% of the observed Dstvariance is predictable in our model. Our neural network model suggests that the minimum Dst of a storm is significant in the storm recovery process.

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