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Ionospheric vertical total electron content prediction model in low-latitude regions based on long short-term memory neural network

引用

Chinese Physics B 2022年第8期31卷 347-358页

作者： Tong-Bao Zhang Hui-Jian Liang Shi-Guang Wang Chen-Guang Ouyang Department of Precision Instrument Tsinghua UniversityBeijing 100084China State Key Laboratory of Precision Measurement Technology and Instrument Tsinghua UniversityBeijing 100084China Department of Electronic Engineer Tsinghua UniversityBeijing 100084China

Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.

关键词： long-short-term memory neural network equatorial ionosphere vertical total electron content vertical total electron content(vTEC)

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