支持向量机用于电离层foF2的短期区域预报  

On the short-term regional prediction of foF2 based on the support vector machine

作　　者：李美玲[1] 胡耀垓[1] 周晨[1] 赵正予[1] 张援农[1] 刘静[2] 邓忠新[3] 

机构地区：[1]武汉大学电子信息学院,湖北武汉430072 [2]中国地震局地震预测所,北京100036 [3]中国电波传播研究所,山东青岛266107

出　　处：《西安电子科技大学学报》2015年第5期147-153,206,共8页Journal of Xidian University

基　　金：国家自然科学基金资助项目(41327002;41375007);湖北省自然科学基金青年杰出人才资助项目(2011CDA099)

摘　　要：为了提高电离层短期区域预报效果,提出了基于支持向量机方法考虑太阳活动、地磁活动、中高层大气、地理位置等因素对电离层的影响.对中国地区电离层F2层临界频率(foF2)提前1h的区域预报模型,将支持向量机的预报模型与输入同样参数的反向传播神经网络和国际参考电离层模型从多方面进行对比分析,结果显示,支持向量机模型的年平均预报相对误差相对神经网络和国际参考电离层模型在太阳活动高年分别降低了2.5%和9.6%,在太阳活动低年分别降低了1.9%和7.5%.在低纬度地区,支持向量机模型的预报优势更加显著,在高年和低年相对反向传播神经网络分别降低了3.2%和2.7%.对暴时,支持向量机模型也表现出一定的预报能力.这表明支持向量机模型应用在中国区域电离层foF2短期预报上,相对反向传播神经网络和国际参考电离层模型更有优势.Ionospheric short-term forecasting is very important to radio communication,navigation and radar systems.In this paper,in order to improve the regional prediction accuracy of ionosphere,a model of regional prediction of the ionospheric F2 layer critical frequency in China area 1hour in advance is set up based on the support vector machine（Support Vector Machine,referred to as SVM for short）method.In this model,the influence of solar activity,geomagnetic activity,the upper atmosphere,geographical location and other factors on the ionosphere is taken into consideration.Results of this model is compared to Back-Propagation referred to as BP for short the neural network of the same input parameters and the IRI model（International Reference Ionosphere,referred to as IRI for short）.The results show that the average relative error of annual prediction of SVM in high solar activity years decreases by 2.5% and 9.6%,respectively,compared with the neural network and the IRI models and in low solar activity decreases by1.8% and 7.5%,respectively.In the low latitude area,the prediction of SVM has more significant advantages over the BP neural network.In the high and low solar activity years it decreases by 3.2% and2.7%,respectively.During the storm time SVM also shows a relatively good prediction ability.This proves that the developed model based on SVM in the paper has more advantages over the BP neural network and IRI model.

关 键 词：支持向量机 电离层foF2 区域预报 对比分析 

分 类 号：P352[天文地球—空间物理学]