Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning‐Based Multi‐Model Ensemble Method

Ren, Xiaodong; Yang, Pengxin; Mei, Dengkui; Liu, Huan; Xu, Guozhen; Dong, Yue

doi:10.1029/2022sw003231

Cited by 14 publications

(6 citation statements)

References 30 publications

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“…The data used in this paper, including the GIM-TEC, F10.7, ap, Kp, and Dst, as well as the program for the model, are available at Ren (2024).…”

Section: Data Availability Statementmentioning

confidence: 99%

Deep Learning‐Based Prediction of Global Ionospheric TEC During Storm Periods: Mixed CNN‐BiLSTM Method

Ren,

Zhao,

Ren

et al. 2024

Space Weather

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The application of deep learning in high‐precision ionospheric parameter prediction has become one of the focus in space weather research. In this study, an improved model called Mixed Convolutional Neural Networks (CNN)—Bi‐Long Short Term Memory is proposed for predicting future ionospheric Total Electron Content (TEC). The model is trained using the longest available (25 years) Global Ionospheric Maps‐TEC and evaluated the accuracy of ionospheric storm predictions. The results indicate that using historical TEC in the solar‐geographical reference frame as input driving data achieves higher prediction accuracy compared to that in the geocentric coordinate system. Additionally, by comparing different input parameters, it is found that incorporating the Kp, ap, and Dst indices as inputs to the model effectively improves its accuracy, especially in long‐term forecasting where R2 increased by 3.49% and Root Mean Square Error decreased by 13.48%. Compared with BiLSTM‐Deep Neural Networks (DNN) and CNN‐BiLSTM, the Mixed CNN‐BiLSTM model has the highest prediction accuracy. It suggests that the utilization of CNN modules for processing spatial information, along with the incorporation of DNN modules to incorporate geomagnetic indices for result correction. Moreover, in short‐term predictions, the model accurately forecasts the evolution process of ionospheric storms. When extending the predicted length, although there are cases of prediction errors, the model still captures the entire process of ionospheric storms. Furthermore, the predicted results are significantly influenced by longitude, magnetic latitude, and local time.

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“…The data used in this paper, including the GIM-TEC, F10.7, ap, Kp, and Dst, as well as the program for the model, are available at Ren (2024).…”

Section: Data Availability Statementmentioning

confidence: 99%

Deep Learning‐Based Prediction of Global Ionospheric TEC During Storm Periods: Mixed CNN‐BiLSTM Method

Ren,

Zhao,

Ren

et al. 2024

Space Weather

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“…Training the model with imbalanced EIC data, the mean square error (MSE) and a focal loss (L4) are utilized as loss functions to be minimized, for comparatively studying the improvement of the imbalanced regression by different loss functions. Many other machine learning models dealt with the imbalance problem by manually selecting only extreme events to improve the performance, for example, (Hu et al., 2023; Ren et al., 2023). We avoid providing such a priori knowledge to the predictive model, such that our model is robust to provide continuous predictions/forecasts in the real operational environment.…”

Section: Model Descriptionmentioning

confidence: 99%

The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model

Cao,

Chu,

Bortnik

et al. 2023

Space Weather

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A predictive model for the variation of ionospheric currents is of great scientific and practical importance to our modern industrial society. To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neural network model trained on the Equivalent Ionospheric Current (EIC) data from 1st January 2007 to 31st December 2019. Due to the highly imbalanced nature of the ionospheric currents data, which means that the data of extreme events are much less than those of quiet times, we utilized different loss functions to improve the model performance. Our model demonstrates the potential to predict the active events of ionospheric currents reasonably well (e.g., EICs during substorms) within a timescale of a few minutes. Although the data used for training are measurements over the North American and Greenland sectors, our model is not only able to predict EICs within this region, but is also able to provide a promising out‐of‐sample prediction on a global scale.

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“…They utilized seven different features to model 170 geomagnetic storm events. The results of their study showed that the DLMEM model was able to reduce the RMSE by an average of 43.6% compared to their previously presented model, Ion-LSTM [29]. LSTM is a powerful tool for handling temporal variations due to its memory retention capabilities, but it has limitations when dealing with problems that have both temporal and spatial characteristics.…”

Section: Introductionmentioning

confidence: 99%

Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model

Yang,

Huang,

Xia

et al. 2024

Remote Sensing

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In this paper, we propose a global ionospheric total electron content (TEC) maps (GIM) prediction model based on deep learning methods that is both straightforward and practical, meeting the requirements of various applications. The proposed model utilizes an encoder-decoder structure with a Convolution Long Short-Term Memory (ConvLSTM) network and has a spatial resolution of 5° longitude and 2.5° latitude, with a time resolution of 1 h. We utilized the Center for Orbit Determination in Europe (CODE) GIM dataset for 18 years from 2002 to 2019, without requiring any other external input parameters, to train the ConvLSTM models for forecasting GIM 1, 2, and 3 days in advance. Using the CODE GIM data from 1 January 2020 to 31 December 2023 as the test dataset, the performance evaluation results show that the average root mean square errors (RMSE) for 1, 2 and 3 days of forecasts are 2.81 TECU, 3.16 TECU, and 3.41 TECU, respectively. These results show improved performance compared to the IRI-Plas model and CODE’s 1-day forecast product c1pg, and comparable to CODE’s 2-day forecast c2pg. The model’s predictions get worse as the intensity of the storm increases, and the prediction error of the model increases with the lead time.

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Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning‐Based Multi‐Model Ensemble Method

Cited by 14 publications

References 30 publications

Deep Learning‐Based Prediction of Global Ionospheric TEC During Storm Periods: Mixed CNN‐BiLSTM Method

Deep Learning‐Based Prediction of Global Ionospheric TEC During Storm Periods: Mixed CNN‐BiLSTM Method

The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model

Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model

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