Prediction of Ionospheric TEC Based on the NARX Neural Network

Effective prediction of ionospheric total electron content (TEC) is very important for Global Navigation Satellite System (GNSS) positioning and other related applications. This paper proposes an ionospheric TEC prediction method using the nonlinear autoregressive with exogenous input (NARX) neural...

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Autores principales:	Liu Guoyan, Gao Wang, Zhang Zhengxie, Zhao Qing
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/aedf13fbfc2d43178741fe867b092dae
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spelling	oai:doaj.org-article:aedf13fbfc2d43178741fe867b092dae2021-11-08T02:36:25ZPrediction of Ionospheric TEC Based on the NARX Neural Network1563-514710.1155/2021/7188771https://doaj.org/article/aedf13fbfc2d43178741fe867b092dae2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7188771https://doaj.org/toc/1563-5147Effective prediction of ionospheric total electron content (TEC) is very important for Global Navigation Satellite System (GNSS) positioning and other related applications. This paper proposes an ionospheric TEC prediction method using the nonlinear autoregressive with exogenous input (NARX) neural network, which uses previous TEC data and external time parameter inputs to establish a TEC prediction model. During the years of different solar activities, 12 datasets of 3 stations with different latitudes are used for experiments. Each dataset uses the first 120 days for training and the next 20 days for testing. For each test dataset, a sliding window strategy is adopted in the prediction process, wherein the TEC of future 2 days are predicted by the true TEC values of the previous 2 days. The results show that in the year with active solar activity (2011), the TEC prediction with the NARX network can improve the accuracy by 32.3% and 43.5%, compared with the autoregressive integrated moving average (ARIMA) model and the 2-day predicted TEC product, named C2PG. While in the year with calm solar activity (2017), the prediction accuracy can be improved by 20.7% and 22.7%.Liu GuoyanGao WangZhang ZhengxieZhao QingHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Liu Guoyan Gao Wang Zhang Zhengxie Zhao Qing Prediction of Ionospheric TEC Based on the NARX Neural Network
description	Effective prediction of ionospheric total electron content (TEC) is very important for Global Navigation Satellite System (GNSS) positioning and other related applications. This paper proposes an ionospheric TEC prediction method using the nonlinear autoregressive with exogenous input (NARX) neural network, which uses previous TEC data and external time parameter inputs to establish a TEC prediction model. During the years of different solar activities, 12 datasets of 3 stations with different latitudes are used for experiments. Each dataset uses the first 120 days for training and the next 20 days for testing. For each test dataset, a sliding window strategy is adopted in the prediction process, wherein the TEC of future 2 days are predicted by the true TEC values of the previous 2 days. The results show that in the year with active solar activity (2011), the TEC prediction with the NARX network can improve the accuracy by 32.3% and 43.5%, compared with the autoregressive integrated moving average (ARIMA) model and the 2-day predicted TEC product, named C2PG. While in the year with calm solar activity (2017), the prediction accuracy can be improved by 20.7% and 22.7%.
format	article
author	Liu Guoyan Gao Wang Zhang Zhengxie Zhao Qing
author_facet	Liu Guoyan Gao Wang Zhang Zhengxie Zhao Qing
author_sort	Liu Guoyan
title	Prediction of Ionospheric TEC Based on the NARX Neural Network
title_short	Prediction of Ionospheric TEC Based on the NARX Neural Network
title_full	Prediction of Ionospheric TEC Based on the NARX Neural Network
title_fullStr	Prediction of Ionospheric TEC Based on the NARX Neural Network
title_full_unstemmed	Prediction of Ionospheric TEC Based on the NARX Neural Network
title_sort	prediction of ionospheric tec based on the narx neural network
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/aedf13fbfc2d43178741fe867b092dae
work_keys_str_mv	AT liuguoyan predictionofionospherictecbasedonthenarxneuralnetwork AT gaowang predictionofionospherictecbasedonthenarxneuralnetwork AT zhangzhengxie predictionofionospherictecbasedonthenarxneuralnetwork AT zhaoqing predictionofionospherictecbasedonthenarxneuralnetwork
_version_	1718443128006901760

Prediction of Ionospheric TEC Based on the NARX Neural Network

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