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摘要
Thispaper presents the development of Neural Network (NN) model for the prediction of the F2 layer critical frequency (foF2) at three ionosonde stations near the magnetic equator of Southeast Asia. Two of these stations including Chiang Mai (18.76 degrees N, 98.93 degrees E, dip angle 12.7 degrees N) and Kototabang (0.2 degrees S, 100.3 degrees E, dip angle 10.1 degrees S) are at the conjugate points while Chumphon (10.72 degrees N, 99.37 degrees E, dip angle 3.0 degrees N) station is near the equator. To produce the model, the feed forward network with backpropagation algorithm is applied. The NN is trained with the daily hourly values of foF2 during 2004-2012, except 2009, and the selected input parameters, which affect the foF2 variability, include day number (DN), hour number (HR), solar zenith angle (C), geographic latitude (theta), magnetic inclination (I), magnetic declination (D) and angle of meridian (M) relative to the sub-solar point, the 7-day mean of F10.7 (F10.7_7), the 81-day mean of SSN (SSN_81) and the 2-day mean of Ap (Ap_2). The foF2 data of 2009 and 2013 are then used for testing the NN model during the foF2 interpolation and extrapolation, respectively. To examine the performance of the proposed NN, the root mean square error (RMSE) of the observed foF2, the proposed NN model and the IRI-2012 (CCIR and URSI options) model are compared. In general, the results show the same trends in foF2 variation between the models (NN and IRI-2012) and the observations in that they are higher during the day and lower at night. Besides, the results demonstrate that the proposed NN model can predict the foF2 values more closely during daytime than during nighttime as supported by the lower RMSE values during daytime (0.5 <= RMSE <= 1.0 for Chumphon and Kototabang, 0.7 <= RMSE <= 1.2 at Chiang Mai) and with the highest levels during nighttime (0.8 <= RMSE <= 1.5 for Chumphon and Kototabang, 1.2 <= RMSE <= 2.0 at Chiang Mai). Furthermore, the NN model predicts the foF2 values more accurately than the IRI model at the three sites on average, as clearly seen on the yearly RMSE averages. The RMSE values of NN model are lower than those of both CCIR and URSI options, and in terms of the yearly percentage improvements, the NN model gives improvement of around 10-15% in 2009 and 10% in 2013 for Chiang Mai, 20-25% in 2009 and 5-10% in 2013 for Chumphon, and around 18-25% in 2009 and 20-30% in 2013 at Kototabang. Although the NN model predicts the foF2 values closely to the observed data and produces more accurate prediction than the WI models, in some cases, the IRI model performs better than the NN model. Hence, there is still room for further improvement of the proposed NN model.
- 出版日期2017-6-15
