A Bidirectional Deep-Learning Algorithm to Forecast Regional Ionospheric TEC Maps

Krishna, K. Siva; Ratnam, D. Venkata; Sivavaraprasad, G.

doi:10.1109/jstars.2022.3180940

Cited by 24 publications

(13 citation statements)

References 28 publications

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“…In recent years, various deep learning methods have been utilized for TEC prediction (Li et al., 2022; Liu et al., 2022; Mallika et al., 2018; Ren et al., 2022; Ruwali et al., 2020; Sivakrishna et al., 2022; Tang, Li, Ding, et al., 2022; Tang, Li, Yang, & Ding, 2022; Ulukavak, 2021; Xiong et al., 2021). These studies demonstrate that deep learning methods are well‐suited for handling complex and non‐linear features in the ionosphere.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach

Jeong,

Lee,

Kil

et al. 2024

Space Weather

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This study evaluates the performance of deep learning approach in the prediction of the ionospheric total electron content (TEC) during magnetically quiet periods. Two deep learning techniques, long short‐term memory (LSTM) and convolutional LSTM (ConvLSTM), are employed to predict TEC values 24 hr ahead in the vicinity of the Korean Peninsula (26.5°–40°N, 121°–134.5°E). The LSTM method predicts TEC at a single point based on time series of data at that point, whereas the ConvLSTM method simultaneously predicts TEC values at multiple points using spatiotemporal distribution of TEC. Both the LSTM and ConvLSTM models are trained using the complete regional TEC maps reconstructed by applying the Deep Convolutional Generative Adversarial Network–Poisson Blending (DCGAN‐PB) method to observed TEC data. The training period spans from 2002 to 2018, and the model performance is evaluated using 2019 data. Our results show that the ConvLSTM method outperforms the LSTM method, generating more reliable TEC maps with smaller root mean square errors when compared to the ground truth (DCGAN‐PB TEC maps). This outcome indicates that deep learning models can improve the prediction accuracy of TEC at a specific point by taking into account spatial information of TEC. We conclude that ConvLSTM is a reliable and efficient approach for the prompt ionospheric prediction.

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Section: Introductionmentioning

confidence: 99%

Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach

Jeong,

Lee,

Kil

et al. 2024

Space Weather

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“…Therefore, the regional model has been the mainstream of research in recent years, and the proposal of the regional model is also the inevitable demand of future communication system applications (Wang, Yang, & Yan, 2021). In decades, researchers have gradually proposed specialized TEC regional prediction models in China (Xiong et al., 2021), Japan (Mallika et al., 2019), India (Sivakrishna et al., 2022), Korean Peninsula (Jeong et al., 2022), South Africa (Ssessanga et al., 2019), Antarctic region (Yao et al., 2021), low latitude region (Zewdie et al., 2021), and ocean (Ren et al., 2022). On the other hand, from the perspective of the methods used for modeling, the spatial reconstruction of TEC can be divided into mathematical and machine learning‐based methods.…”

Section: Introductionmentioning

confidence: 99%

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

Wang,

Liu,

Shi

2023

Space Weather

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In order to achieve more accurate spatial reconstruction of ionospheric total electron content (TEC) and promote improved satellite positioning and ranging applications, a high accuracy spatial reconstruction (HASR) method for TEC is proposed based on the surface theory. The core theory of this method is as follows: (a) Any surface can be uniquely determined by its first and second fundamental quantities; (b) By direct difference approximation, differential equations are transformed into algebraic equations to solve Gauss equations faster. At the same time, taking parts of Europe as an example, the proposed HASR method is used to determine the correlation coefficients and the number of iterations of the model by using the relative root mean square error (RRMSE) as the evaluation criterion. The statistical results show that the TEC predicted by the HASR method is highly consistent with the actual observed values of ionospheric observation stations, and the prediction RRMSE is 9.75%. Compared with the Kriging interpolation with scale factor, the prediction accuracy of the HASR method is improved by 8.5%. We hope this method can provide ideas for the spatial reconstruction of other ionospheric parameters and further promote the realization of complete and accurate space weather forecast.

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“…6 Even when input sequences are indestructible or stochastic in nature, this algorithm can bridge time breaks in more steps without sacrificing brief time break capabilities. 7,8 The long-term dependencies of RNN, using current data, can make ARTICLE pubs.aip.org/aip/adv precise predictions; however, it cannot predict data stored in longterm memory. These dependencies are resolved using the LSTM technique, which makes the input of a current step the outcome of the previous step.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for prediction of total electron content and classification of ionospheric scintillations over Visakhapatnam region

Nimmakayala,

Dutt V.B.S

2023

AIP Advances

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Ionospheric scintillations, which are due to ionospheric plasma density anomalies, negatively impact trans-ionospheric signals and the positioning accuracy of the global navigation satellite system (GNSS). One of the crucial variables for comprehending space weather conditions is the total electron content (TEC) of the ionosphere. It is vital to predict the ionospheric TEC before making efforts to enhance the GNSS system. In this article, the long short-term memory machine learning approach for TEC prediction is presented, based on which the ionospheric phase scintillations are identified and classified using popular classifiers: support vector machines and decision trees. In this article, the comparative analysis of these classifiers is presented using the standard performance metrics: accuracy, recall, precision, and F1 score.

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A Bidirectional Deep-Learning Algorithm to Forecast Regional Ionospheric TEC Maps

Cited by 24 publications

References 28 publications

Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach

Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

Machine learning approach for prediction of total electron content and classification of ionospheric scintillations over Visakhapatnam region

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