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

Abstract

In order to enhance the reproduction of the recovery phase Dst index 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 Dst values before the minimum Dst in 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, $sqrt {n{V^2}}$, 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 Dst strengths outside the training data set. Extremely good agreement between the measured Dst and the modeled Dst has 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 Dst variance 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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Correspondence to Samuel Kugblenu.

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Keywords

  • Solar Wind
  • Root Mean Square Error
  • Hide Layer
  • Storm Event
  • Recovery Phase