An attempt to study long-term variation of sporadic E layers using neural networks

Strong positive correlation between sporadic E layers and the solar activity and the long-term declining trend of Es were found in this paper. Then the feed-forward back propagation neural networks (NNs) were used to simulate the long-term variation of Es at four stations and predict foEs yearly average values. The inputs used for NNs are the yearly mean values of foEs in the daytime of the past ten years and the yearly averaged data of solar 10.7 cm radio flux (F107) of the present year, and the output is the present yearly mean value of daytime foEs. The outputs of trained NNs have high correlation with the desired values and the foEs yearly mean values predicted by NNs have good agreement with the observed data. The results indicate that NNs can make full use of the observed data to simulate the long variation rule of Es. Also, the results confirm the effect of solar activity on Es.