| 内容提要: |
As a basis of the long-term prediction of frequency
selecting for HF communication, a model based on statistical machine learning
method is proposed to improve the accuracy of predicting the monthly median
ionospheric critical frequency of the F2 layer (identified as foF2), which is
one of the key parameters for predicting usable frequencies for HF
communication. The annual dynamic variation map of the proposed model is
achieved by the two solar activity parameters of the 10.7-cm solar radio flux
and sunspot number. And the geomagnetic dip latitude and its modified value are
first together chosen as features of the geographical spatial variation for
reconstructing spatial dynamic variation map. The proposed model can provide
higher prediction accuracy for foF2 over Asia. Compared with the international
reference ionosphere (IRI) model with CCIR and URSI coefficients (identified as
IRI-CCIR and IRI-URSI), the root-mean-square error of the proposed model is
reduced by 0.27MHz and 0.23MHz respectively, and the accuracy is improved by
2.90% and 1.85% respectively. |