利用双向LSTM和关注机制预测电离层of2

IF 3.8 2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS
Jun Tang, Dengpan Yang, Mingfei Ding
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引用次数: 0

摘要

电离层F2层临界频率(foF2)是电离层重要的特征参数。本文提出了一种基于双向长短期记忆(BiLSTM)和注意机制的深度学习模型,用于预测foF2参数。模型输入2015 - 2017年全球电离层电离层探空站foF2、地理经纬度、世界时间(UT)、地磁活动指数和太阳活动指数。在不同纬度、季节和地磁条件下分析了模型的优越性。结果表明,基于注意机制的双向长短期记忆模型(BiLSTM‐attention)的预测性能优于其他模型。该模型在高纬度地区的预报效果最佳。在高纬度地区,BiLSTM‐Attention模型的均方根误差(RMSE)和相关系数(R)分别为0.539 MHZ和0.908 MHZ。RMSE分别比国际参考电离层(IRI)、LSTM和BiLSTM模式低25.243%、18.209%和11.203%。四季预报结果表明,该模型在冬季更适用。与IRI模型相比,BiLSTM‐Attention模型在春季、夏季、秋季和冬季的RMSE分别降低了24.344%、21.181%、25.058%和30.948%。在磁平静期、磁温和期和磁暴期,BiLSTM‐Attention模型的预报效果得到了提高。在静磁日,改进效果更为明显,RMSE较IRI模型降低了27.462%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism
Abstract The critical frequency of ionospheric F2 layer (foF2) is an important ionospheric characteristic parameter. In this paper, a deep learning model based on Bidirectional long short‐term memory (BiLSTM) and attention mechanism is implemented for predicting the foF2 parameter. The inputs of models are the foF2 of globally available ionospheric ionosonde stations, geographic longitude and latitude, world time (UT), geomagnetic activity index, and solar activity index from 2015 to 2017. The superiority of the model is analyzed from different latitudes, seasons, and geomagnetic conditions. The results show that the prediction performance of the Bidirectional long short‐term memory model based on attention mechanism (BiLSTM‐Attention) is better than other models. The performance of the prediction model is optimal at high latitudes. The root mean square error (RMSE) and correlation coefficient (R) of the BiLSTM‐Attention model are 0.539 MHZ and 0.908 MHz at high latitudes, respectively. In terms of RMSE, it is 25.243%, 18.209%, and 11.203% lower than those of the international reference ionosphere (IRI), LSTM, and BiLSTM models, respectively. The prediction results of the four seasons show that the models are more applicable in winter. Compared with the IRI model, the RMSE of the BiLSTM‐Attention model in spring, summer, autumn, and winter is reduced by 24.344%, 21.181%, 25.058%, and 30.948%, respectively. The prediction effect of the BiLSTM‐Attention model is improved in the magnetic quiet period, the magnetic moderate period and the magnetic storm period. Also, the improvement effect is more obvious in the magnetostatic day, and the RMSE is reduced by 27.462% compared with the IRI model.
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来源期刊
CiteScore
5.90
自引率
29.70%
发文量
166
审稿时长
>12 weeks
期刊介绍: Space Weather: The International Journal of Research and Applications (SWE) is devoted to understanding and forecasting space weather. The scope of understanding and forecasting includes: origins, propagation and interactions of solar-produced processes within geospace; interactions in Earth’s space-atmosphere interface region produced by disturbances from above and below; influences of cosmic rays on humans, hardware, and signals; and comparisons of these types of interactions and influences with the atmospheres of neighboring planets and Earth’s moon. Manuscripts should emphasize impacts on technical systems including telecommunications, transportation, electric power, satellite navigation, avionics/spacecraft design and operations, human spaceflight, and other systems. Manuscripts that describe models or space environment climatology should clearly state how the results can be applied.
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