Forecasting of ionospheric TEC for different latitudes, seasons and solar activity conditions based on OKSM

Mukesh, R.; Karthikeyan, V.; Soma, P.; Sindhu, P.

doi:10.1007/s10509-020-3730-x

Cited by 19 publications

(13 citation statements)

References 26 publications

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“…Many studies have shown that the performance of ionospheric TEC prediction models varies with the season (Mukesh et al., 2020; Tebabal et al., 2019). In this section, the performances of all the proposed models are evaluated in different season and year.…”

Section: Igs‐tec Map Predictionsmentioning

confidence: 99%

Prediction of Global Ionospheric TEC Based on Deep Learning

Zhou

Liao

et al. 2022

Space Weather

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The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS‐TEC maps are established based on testing several different long short‐term memory (LSTM) network (LSTM)‐based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. We find that a Multi‐step auxiliary algorithm based prediction model performs best. It can effectively predict the global ionospheric IGS‐TEC in the next 6 days (the mean absolute deviation (MAD) and root mean square error (RMSE) are 2.485 and 3.511 TECU, respectively) compared to the IRI (the MAD and RMSE are 4.248 and 5.593 TECU). The analyses of four geomagnetic storm events are completely separate from the time range of the training set, so as to further validate the performance of the model. The International Reference Ionosphere model is used as a reference for the performance of our predictive model, and a rotated persistence is estimated by time‐shift algorithm of IGS‐TEC. The result suggests that the Multi‐step auxiliary prediction model has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm and quiet time.

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Section: Igs‐tec Map Predictionsmentioning

confidence: 99%

Prediction of Global Ionospheric TEC Based on Deep Learning

Zhou

Liao

et al. 2022

Space Weather

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“…Previous studies indicated TEC prediction model performance varies seasonally (Mukesh et al, 2020;Ruwali et al, 2020;Song et al, 2018;Tebabal et al, 2018Tebabal et al, , 2019. Therefore, all considered models were operated in each season to investigate their performance throughout the solar year.…”

Section: Seasonal Variation In Model Performancementioning

confidence: 99%

Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China

et al. 2021

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• A novel extended encoder-decoder long short-term memory neural network (ED-LSTME) for ionospheric TEC forecasting over China is developed • ED-LSTME shows a strong capability in improving TEC forecasting at different geographical locations, seasons, and geomagnetic conditions • ED-LSTME is robust and outperforms all the six selected baselines when comparing with the models in terms of their performance Accepted Article This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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“…Te solar parameters that afect TEC are solar radio fux F10.7 and sunspot number (SSN). Planetary index (Kp, Ap) and Disturbance storm time index (Dst) are used as geomagnetic parameters in the study of ionospheric TEC variations [23][24][25][26]. Also, the interplanetary magnetic feld (IMF) data By and Bz along with plasma speed (Vp) and proton density (Np) have shown to yield better results in prediction of TEC maps [27].…”

Section: Input Parameters For the Lstm Modelmentioning

confidence: 99%

Forecasting of Ionospheric Total Electron Content Data Using Multivariate Deep LSTM Model for Different Latitudes and Solar Activity

Shenvi

Virani

2023

Journal of Electrical and Computer Engineering

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The ionospheric state is becoming increasingly important to forecast for the reliable operation of terrestrial and space-based radio-communication systems which are influenced by ionospheric space weather. In this study, we have investigated and tested a multivariate long short-term memory (LSTM) deep learning model for its forecasting accuracy over different latitudinal regions during the solar quiet and solar active years. We also tested its prediction capability during the occurrence of a geomagnetic storm. Four stations qaq1 (60.7°N, 46.04°W), baie (49.18°N, 68.26°W), mas1 (27.76°N, 15.63°W), and bogt (4.64°N, 74.08°W) in the northern hemisphere were used in this study. To optimize the feature extraction process, we used heat map to find the correlation between TEC and the various exogenous parameters and finally nine correlated parameters were used as inputs to train the LSTM model. The performance of the LSTM model was validated by comparing it with the multilayer perceptron (MLP) machine learning algorithm using root mean square error (RMSE) and mean absolute error (MAE) as evaluation indices. The results showed an accuracy improvement of 70% and 64% over MLP during the solar quiet and active years, respectively. The prediction accuracy of our LSTM model was also 74% better than MLP during the geomagnetic storm event. These findings demonstrate the effectiveness of the developed LSTM model and the right selection of the exogenous parameters in estimating TEC, and suggest that this LSTM model can be used for short-term TEC forecasting.

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Forecasting of ionospheric TEC for different latitudes, seasons and solar activity conditions based on OKSM

Cited by 19 publications

References 26 publications

Prediction of Global Ionospheric TEC Based on Deep Learning

Prediction of Global Ionospheric TEC Based on Deep Learning

Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China

Forecasting of Ionospheric Total Electron Content Data Using Multivariate Deep LSTM Model for Different Latitudes and Solar Activity

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