Forecasting of Ionospheric Total Electron Content Data using Autoregressive Distributed Lag Model for Mid-Latitude Region During Solar Minimum and Maximum

Shenvi, Nayana; Virani, H. G.; Chandrasekhar, E.

doi:10.1109/icdsaai55433.2022.10028905

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…These models effectively predict the state of the ionosphere during quiet times but are not very accurate during periods of Bulletin of Electr Eng & Inf ISSN: 2302-9285  Prediction of ionospheric total electron content data using NARX neural network model (Nayana N. Shenvi) 549 high ionospheric activity. Several time-series models developed for prediction of TEC include fourier series [6], discrete cosine transform [7], and autoregressive distributed lag [8]. However, these models are not effective in predicting the non-stationary fluctuations present in the ionosphere.…”

Section: Introductionmentioning

confidence: 99%

Prediction of ionospheric total electron content data using NARX neural network model

Shenvi,

Virani

2024

Bulletin EEI

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Successful prediction of ionospheric total electron content (TEC) data will help in correction of positioning errors in global navigation satellite systems (GNSS) caused by the ionosphere. This research paper proposes a prediction model for ionospheric TEC using a nonlinear autoregressive with exogenous inputs (NARX) neural network that utilizes past TEC data alongwith solar and geomagnetic indices namely F10.7, disturbed storm (Dst), Kp, Ap, and time of the day. We assess the prediction capability of our model at different latitudes during different solar activity years. We compare our results with another NARX model which uses previous TEC data along with time of the day, day of the year and season as exogenous parameters. The results show that for the solar minimum year the TEC prediction accuracy improves by 35.71% and for the solar maximum year it improves by 31.20%. The results using root mean square error (RMSE), mean absolute error (MAE), correlation coefficient, and symmetric mean absolute percentage error (sMAPE) clearly indicate that solar and geomagnetic indices along with time of the day help in enhancing prediction accuracy of TEC across different latitudinal regions during both solar minimum and maximum years.

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Section: Introductionmentioning

confidence: 99%

Prediction of ionospheric total electron content data using NARX neural network model

Shenvi,

Virani

2024

Bulletin EEI

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“…Te traditional ionospheric error mitigation approaches like the International Reference Ionosphere (IRI) model [3], the Klobuchar model [4], the NeQuick model [5] show limitations during complex ionospheric dynamics. Statistical methods like autoregressive [6], autoregressive moving average (ARMA) [7], autoregressive distributed lag (ARDL) model [8] have been developed in the past for forecasting regional short-term ionospheric TEC. Over the years, several neural network models have been developed for prediction of TEC and various related parameters at regional levels.…”

Section: Introductionmentioning

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 Total Electron Content Data using Autoregressive Distributed Lag Model for Mid-Latitude Region During Solar Minimum and Maximum

Cited by 2 publications

References 26 publications

Prediction of ionospheric total electron content data using NARX neural network model

Prediction of ionospheric total electron content data using NARX neural network model

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

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