Forecasting geomagnetic activity K_p index of space environment with H_p

Geomagnetic disturbance can cause a series of changes for near-Earth space environment, including the ionosphere and magnetosphere, and K_p index is the important reference index for disturbance of space weather. H_p component data monitored by GOES-8 satellite of geosynchronous orbit was used in this paper. By analyzing statistical relation between K_p index and H_p component width of fluctuation, it is shown that the change of H_p component and K_p index have good correlation. Based on the change of H_p component, K_p index in the same time interval is calculated, and then K_p index forecast model is established by means of regression analysis and RBF neural networks. Monitoring results show that the prediction method has certain validity and practicability especially that K_p index calculated by artificial neural networks is quite consistent with its measured values. By using this method, it is able to forecast geomagnetic disturbance quickly, and to provide reference in time for space weather guarantee not relying on the data from geomagnetic diction. In addition, China is going to launch FY-4 satellite, on which a magnetic field detector will be carried. Hence, this research will provide the foundation for application using FY-4 data in the future.