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This paper describes a neural network-based model developed to predict geomagnetic storms time K index as measured at a magnetic observatory located in Hermanus (34°25 S; 19°13 E), South Africa. The parameters used as inputs to the neural network were the solar wind particle density N, the solar wind velocity V, the interplanetary magnetic field (IMF) total average field B t  as well as the IMF B z  component. Averaged hourly OMNI-2 data comprising storm periods extracted from solar cycle 23 (SC23) were used to train the neural network. The prediction performance of this model was tested on some moderate to severe storms (with K≥5) that were not included in the training data set and the results are compared to the prediction of the global geomagnetic Kp index. The model results show a good predictability of the Hermanus storm time K index with a correlation coefficient of 0.8.

Empirical modeling of the storm time geomagnetic indices: a comparison between the local K and global Kp indices