Real time Kp predictions from solar wind data using neural networks

Multilayer feed-forward neural network models are developed to make three-hour predictions of the planetary magnetospheric Kp index. The input parameters for the networks are the B-z-component of the interplanetary magnetic field, the solar wind density n, and the solar wind velocity V, given as three-hour averages. The networks are trained with the error back-propagation algorithm on data sequences extracted from the 21(st) solar cycle. The result is a hybrid model consisting of two expert networks providing Kp predictions with an RMS error of 0.96 and a correlation of 0.76 in reference to the measured Kp values. This result can be compared with the linear correlation between V(t) and Kp(t + 3 hours) which is 0.47. The hybrid model is tested on geomagnetic storm events extracted from the 22(nd) solar cycle. The hybrid model is implemented and real time predictions of the planetary magnetospheric Kp index are available at http://www.astro.lu.se/similar to fredrikb. (C) 2000 Elsevier Science Ltd. All rights reserved.