全球定位系统2024，Vol.49Issue(5)：88-96,9.DOI:10.12265/j.gnss.2024140
基于随机森林算法的中国及周边区域电离层foF2预测模型
A random forest-based prediction model for ionospheric foF2 in China and surrounding regions
摘要
Abstract
The square of the critical frequency of the ionospheric F2 layer(foF2)is proportional to the peak electron density(NmF2)and serves as a crucial parameter affecting the performance of Global Navigation Satellite Systems(GNSS).Enhancing the prediction accuracy of foF2 is essential for optimizing GNSS broadcast ionospheric models,thereby improving the positioning accuracy of GNSS.This study develops an ionospheric foF2 prediction model for China and its surrounding regions using the random forest algorithm,based on data from 18 ionosonde stations of the China Research Institute of Radiowave Propagation and COSMIC occultation observations.The model incorporates multiple features,including Universal Time,day of the year,geographic location,solar,and geomagnetic activities.A comparative analysis with the International Reference Ionosphere(IRI-2020)model validates the prediction accuracy of our model.The results indicate that the random forest model reduces the mean absolute error by 14.81%and 17.11%,and the root mean square error by 11.21%and 13.14%,compared to the IRI CCIR and IRI URSI models,respectively.Additionally,the model exhibits superior prediction accuracy under various latitudes,local times,solar,and geomagnetic activity conditions when compared to IRI-2020.This research not only significantly enhances the foF2 prediction accuracy for China and its surrounding regions but also lays a critical foundation for improving the accuracy and reliability of GNSS globally.关键词
电离层foF2/随机森林(RF)/测高仪/COSMIC掩星/国际参考电离层(IRI)/GNSSKey words
ionospheric foF2/random forest/ionosonde/COSMIC occultation/International Reference Ionosphere/GNSS分类
天文与地球科学引用本文复制引用
林子扬,陈龙江,靳睿敏,欧明,杨会贇,姬广旺,崔翔,谷明月..基于随机森林算法的中国及周边区域电离层foF2预测模型[J].全球定位系统,2024,49(5):88-96,9.基金项目
国家自然科学基金(52371354) （52371354）
