Long-term variation trend of east asian ionospheric critical frequency foF2 based on artificial neural network
The trend of F2 layer critical frequency foF2 of ionospheric stations in East Asia mid-latitude is analyzed using artificial neural network method.F107,Ap,Local Time(LT),Month is used as input neurons to represent solar activity,geomagnetic activity,diurnal and seasonal changes respectively.The monthly median value of foF2 is used as output neuron,and the predicted value of foF2 is obtained by training the network.The predicted and observed values are processed and calculated to obtain the long-term variation trend of foF2 in East Asia mid-latitude.The results show that the artificial neural network method can more effectively eliminate the influence of geomagnetic activity on foF2 than the commonly used regression method.There is a clear long-term negative trend in the foF2 of these sites with the increase of the year.And there is no obvious diurnal variation and uniform seasonal variability.These are of great significance for the global ionospheric structure and movement change law,the construction and assimilation of the global ionospheric empirical model,and the ionospheric characteristic parameters and structure prediction.
artificial neural networksionosphereF2 layer critical frequencysolar and geomagnetic activity
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