北京测绘2026，Vol.40Issue(8)：1089-1096,8.DOI:10.19580/j.cnki.1007-3000.2025090018
面向电离层TEC预报的CEEMDAN-GRNN-BiLSTM组合模型
A combined CEEMDAN-GRNN-BiLSTM model for ionospheric TEC forecasting
摘要
Abstract
To enhance the forecasting accuracy of ionospheric total electron content(TEC)and the robustness of forecasting outcomes under geomagnetic disturbance conditions,this paper proposed a combined model integrating complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),generalized regression neural network(GRNN),and bidi-rectional long short-term memory network(BiLSTM)for ionospheric TEC forecasting.The raw ionospheric TEC time series was first decomposed via the CEEMDAN method to obtain a series of intrinsic mode functions(IMFs).The complexity of each IMF component was then evaluated based on the sample entropy algorithm,followed by adaptive recombination of the components.For the recombined high-frequency components,a GRNN-based forecasting model was constructed,while the low-frequency components were modeled and forecasted using the BiLSTM network.Finally,the forecast results of all com-ponents were integrated to reconstruct the complete ionospheric TEC forecast values.To validate the effectiveness of the pro-posed model,measured TEC data from different geomagnetic activity levels and at different latitudes were selected for the experiments.The results show that,during geomagnetically quiet periods,the model achieves a root mean square error(RMSE)of 0.41 TECu,which is 0.11 TECu lower than that of a single-model baseline and 0.05 TECu lower than the other.During geomagnetic storm periods,the model's RMSE is 0.62 TECu,representing reductions of 0.28 TECu and 0.15 TECu relative to the two single-model baselines,respectively.These improvements demonstrate the model's adaptabil-ity and reliability under different space-weather conditions.关键词
自适应噪声完备集合经验模态分解(CEEMDAN)/电离层总电子含量(TEC)预报/广义回归神经网络(GRNN)/双向长短期记忆网络(BiLSTM)Key words
complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)/ionospheric total electron content(TEC)forecasting/generalized regression neural network(GRNN)/bidirectional long short-term memory network(BiLSTM)分类
天文与地球科学引用本文复制引用
宋晓蛟,鲜高强,蒋国政..面向电离层TEC预报的CEEMDAN-GRNN-BiLSTM组合模型[J].北京测绘,2026,40(8):1089-1096,8.基金项目
陕西省自然科学基础研究计划项目(2021JQ562) （2021JQ562）
