全球定位系统2025，Vol.50Issue(6)：21-34,14.DOI:10.12265/j.gnss.2025070
优化BP与LSTM神经网络模型电离层TEC长短期预测
Optimizing BP and LSTM neural network model for long-term and short-term prediction of ionospheric TEC
摘要
Abstract
The total electron content(TEC)of the ionosphere has a significant impact on fields such as radio communication and satellite navigation positioning,therefore,accurate prediction is crucial.In response to the problem of difficult effective prediction of ionospheric TEC,the research introduces deep learning methods and constructs ionospheric TEC prediction models based on back propagation(BP)neural network,K-fold cross validation(KCV)-BP neural network,genetic algorithm(GA)-BP neural network,and long short-term memory(LSTM)neural network using ionospheric TEC grid data(global ionospheric map(GIM))provided by the Center for Orbit Determination in Europe(CODE).These models are used for 1 h short-term prediction and 7-15 d long-term prediction of ionospheric TEC in different latitude regions,different longitude regions and different solar activity period.Indicators such as root mean square error(RMSE),goodness of fit R2,mean absolute percentage error(MAPE)are introduced to evaluate prediction applicability of different models.Research has shown that in short-term forecasting,among different models,the prediction performance from high to low is GA-BP,LSTM,KCV-BP,BP,and ordinary least squares(OLS),and the optimal prediction error is within 1 TECU.In long-term forecasting,OLS has the best prediction performance,especially with a significant advantage at 15 d,while GA-BP has the best long-term timeliness and good prediction stability.The MAPE indicators demonstrate significant differences in the predictive applicability of the model between the northern and southern hemispheres.Finally,when evaluating the applicability of the model to the region,using a single RMSE to measure it is one-sided and requires the comprehensive use of indicators such as R2 and MAPE to measure it.关键词
电离层/总电子含量(TEC)网格点/时间序列预测/优化神经网络/适用性Key words
ionosphere/TEC grid points/time series prediction/adjusted neural network/applicability分类
天文与地球科学引用本文复制引用
田祥雨,石俊杰,郑乃铨,孙佳龙,傅鹏程..优化BP与LSTM神经网络模型电离层TEC长短期预测[J].全球定位系统,2025,50(6):21-34,14.基金项目
江苏省科技计划青年基金(BK20241060) （BK20241060）
江苏省海洋资源开发技术创新中心科技计划(LWKJ-09) （LWKJ-09）
江苏省海洋科技创新项目(JSZRHYKJ202201) （JSZRHYKJ202201）
江苏省水利科技项目(2020058) （2020058）
