A 3D Empirical Model of Electron Density Based on CSES Radio Occultation Measurements

Huang, He; Jiang, Lin; Xu, Shengyuan; Le, Huijun; Guo, Feng; Zhima, Zeren; Liu, Libo; Shen, Xuhui

doi:10.1029/2021sw003018

Cited by 3 publications

(2 citation statements)

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“…H. Huang et al. (2022) constructed a 3D empirical model of electron density based on a China Seismo‐Electromagnetic Satellite for hmF2 and NmF2 prediction. Iban and Şentürk (2022) analyzed the forecasting performance of machine learning models in the foF2, hmF2, and TEC.…”

Section: Introductionmentioning

confidence: 99%

“…Sai Gowtam and Tulasi Ram (2017) established a two-dimensional ionospheric model for peak density of the F2 layer (NmF2) and hmF2 forecasting based on artificial neural networks (ANN), and then successively proposed improved models based on ANN (Tulasi Ram et al, 2018) and three-dimensional (3D) models (Sai Gowtam et al, 2019), these models can capture ionospheric phenomena on a global scale. H. Huang et al (2022) constructed a 3D empirical model of electron density based on a China Seismo-Electromagnetic Satellite for hmF2 and NmF2 prediction. Iban and Şentürk (2022) analyzed the forecasting performance of machine learning models in the foF2, hmF2, and TEC.…”

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confidence: 99%

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A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer

Shi,

Yang,

Wang

et al. 2023

Space Weather

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To achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory. The hybrid model decomposes the hmF2 time data into multiple subsequences through CEEMDAN and reconstructs the subsequences by sample entropy and correlation coefficient into high and low‐frequency sequences, which effectively shortens the calculation time of the model. Then, we determine the optimal hyperparameters of the LSTM models through ISOA, achieving high‐precision forecasting of the hmF2. In single‐step forecasting, the forecasting values of the hybrid model in diurnal and seasonal changes are highly consistent with the observation, which can better capture the severe changes in the hmF2. The model's RMSE, MAE, MAPE, and CC evaluation metrics are 15.86, 11.03 km, 4.76%, and 0.93 in the test set. Compared to IRI, GRU, and LSTM models, taking RMSE as an example, the forecasting accuracy of the models increased by 65.24%, 29.89%, and 29.60%, respectively. In multi‐step forecasting, the proposed model is better at forecasting the changing trend of hmF2, and the forecasting accuracies are significantly better than the IRI model. The data from multiple stations also verified the applicability of the proposed model for hmF2 forecasting. The above results indicate that the hybrid model has high accuracy in hmF2 short‐term forecasting and good applicability in multiple multi‐step forecasting, which can further improve the accurate forecasting of space weather.

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Section: Introductionmentioning

confidence: 99%

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