On the Predictability of FoF2 using Support Vector Machine

Chun Chen, P. Ban, Shuji Sun
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Abstract

This paper proposes a method for forecasting the ionospheric critical frequency, foF2, one day in advance using the support vector machine approach. The output is the predicted foF2 one day ahead. The network is trained to use the ionospheric sounding data at Guangzhou station at high and low solar activity. The performance of the SVM model was verified with observed data. It is shown that the predicted foF2 has agreement with the observed foF2.
基于支持向量机的FoF2可预测性研究
提出了一种利用支持向量机方法提前一天预测电离层临界频率foF2的方法。输出是提前一天预测的foF2。利用广州站在太阳活动高峰和低峰时的电离层探测数据对网络进行了训练。用实测数据验证了SVM模型的性能。结果表明,预测的foF2与实测的foF2基本一致。
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