Modeling of ultraviolet auroral oval boundaries based on neural network technology

The size of auroral oval is closely related to the solar wind, magnetosphere, ionosphere and the coupling process between them. It is very important to establish an accurate boundary model for the space weather prediction and the understanding of the Solar-terrestrial interactions. In this paper, BP neural network and generalized regression neural network (GRNN) is used to construct auroral oval boundary model. The result shows that the auroral oval boundary predict model based on GRNN is more reliable, of which the mean absolute error in equatorial boundary is 0.77–1.20 magnetic latitude (MLAT), and the mean absolute error in poleward boundary is 0.83–1.39 MLAT. Compared with the result of the BP network model, the accurancy of the GRNN model has improved 0.74 and 0.73 MLAT in poleward boundary and equad boundary respectly. Compared with the result of the multiple linear regression model, the accurancy of the GRNN model has improved 0.82 and 0.82 MLAT in poleward boundary and equatoward boundary respectly. In terms of the extrapolation of the model, the extrapolation of GRNN model is better than that of BP model, and is close to the multiple linear regression model.