Linear time-series modeling of the GNSS based TEC variations over Southwest Japan during 2011–2018 and comparison against ARMA and GIM models

Ansari, Kutubuddin; Park, Kwan-Dong; Kubo, Nobuaki

doi:10.1016/j.actaastro.2019.09.017

Cited by 35 publications

(8 citation statements)

References 47 publications

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“…These receivers are located at an average interval of about 25 km and continuously provide GNSS data at a sampling rate of 30 s [ 23 ]. GEONET has been used for long-term observation and monitoring of crustal deformation [ 10 , 24 ], and has been used to solve global and regional issues such as earthquake forecasting, disaster management, high-precision crustal deformation, and strain analysis. It can also be used to evaluate performance and suitability of a regional ionospheric model.…”

Section: Methodsmentioning

confidence: 99%

“…Hernández-Pajares et al [9] analyzed the electron content distribution of the north and south polar ionosphere from 2001 to the beginning of 2019 by using the global ionospheric map (GIM) of VTEC (vertical TEC) computed from GNSS by UPC-IonSAT with a tomographic-kriging combined technique (UQRG GIM), and achieved better results than other methods. Ansari K et al [10] improved the linear time-series model (LTM) and achieved better accuracy than ARMA in southwest Japan. Venkata Ratnam D et al [11] used a model based on spherical harmonic function (SHF) for modeling the ionospheric TEC in low-latitude regions like as India, and the results indicate that the SHF model is capable of estimating the ionospheric delays well.…”

Section: Introductionmentioning

confidence: 99%

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Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014–2019 and the 2016 Kumamoto Earthquake

Yao

Kong

2021

Sensors

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There are a large number of excellent research cases in Global Navigation Satellite System (GNSS) positioning and disaster prediction in Japan region, where the simulation and prediction of total electron content (TEC) is a powerful research method. In this study, we used the data of the GNSS Earth Observation Network (GEONET) established by the Geographical Survey Institute of Japan (GSI) to compare the performance of two regional ionospheric models in Japan, in which the spherical cap harmonic (SCH) model has the best performance. In this paper, we investigated the spatial and temporal variations of ionospheric TEC in Japan and their relationship with latitude, longitude, seasons, and solar activity. The results show that the TEC in Japan increases as the latitude decreases, with the highest average TEC in spring and summer and the lowest in winter, and has a strong correlation with solar activity. In addition, the observation and analysis of ionospheric disturbances over Japan before the 2016 Kumamoto earthquake and geomagnetic storms showed that GNSS observing of ionospheric TEC seems to be very effective in forecasting natural disasters and monitoring space weather.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014–2019 and the 2016 Kumamoto Earthquake

Yao

Kong

2021

Sensors

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“…The second is the ionospheric parameter reconstruction, which is a statistical model of ionospheric parameters such as total electron content (TEC). The accuracy is high in the eld of short-term forecast, for example, time series forecast models such as auto-regression and moving average model (ARMA) (L. Li et al, 2013;Ansari et al, 2019;Lu et al, 2021) and spatial interpolation models like Kriging models (Srinivas et al, 2016;Ghaffari Razin & Moradi, 2021). In recent years, due to their ability to describe complex nonlinear input-output relations, neural networks have been increasingly used for the forecast of ionospheric parameters, especially in the eld of ionospheric TEC forecast, mainly including radial basis function (S. Liu et al, 2020), convolutional neural networks (Ruwali et al, 2021), and long short-term memory (LSTM) networks (Kim et al, 2020(Kim et al, , 2021Tang et al, 2020;Wen et al, 2021;Xiong et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

LSTM-based short-term ionospheric TEC forecast model and position accuracy analysis

Xie

Dai

Zhu

et al. 2022

Preprint

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Ionospheric delay is one of the major error sources in global navigation satellite system (GNSS) single point positioning (SPP). Some empirical models have been proposed to correct ionospheric delay, which, however, are limited by the low accuracy. The single-point time-series-based ionospheric total electron content (TEC) forecast method theoretically introduces model error and the accumulation of forecast error increases during a day. Therefore, based on the regular variation characteristics of the ionosphere, to improve the forecast accuracy of the ionosphere, we propose a long short-term memory (LSTM) short-term forecast model using discrete GNSS data and ionospheric space environment data in the same period of multiple days. The LSTM ionospheric forecast model is constructed based on the 2014 single-site GNSS data from different regions (low, mid, and high latitude regions) of the Crustal Movement Observation Network of China (CMONOC). The results are compared with the Klobuchar model, CMONOC regional ionosphere maps (RIM) data, and GNSS derived TEC measurements. The performance of each model in the SPP is also compared and analyzed to further examine the feasibility of the LSTM ionospheric forecast model. The comprehensive statistical analysis shows that: i) LSTM forecast model is consistent with GNSS-TEC observations at high, mid, and low latitudes, and the forecast error is less than 3 TECu, which is much better than that of the Klobuchar model and RIM model, and is robust to anomalous values. The mean absolute error (MAE) and root mean square error (RMSE) of the LSTM forecast model decrease with increasing latitude. ii) When being used in SPP, the LSTM forecast model brings significant improvement to position accuracy, which is overall better than the RIM and Klobuchar models. The RMSE of 3D position error is 2–4 m for the LSTM forecast model, 2–5 m for the RIM model, and 4–5 m for the Klobuchar model. iii) In terms of geographic location, for 3D position accuracy, it is highest at mid-latitudes followed by high latitudes and worst at low latitudes. For the percentage of 3D position corrections relative to the reference, it is highest at high latitudes followed by mid-latitudes, and lowest at low latitudes. The percentage of LSTM forecast model is 85%-90% at low latitudes, which is better than Klobuchar's 40%-80% and RIM's 75%-85%. Such percentage is over 90% at mid-latitudes, which is comparable to the RIM and about 50% better than the Klobuchar model. It is noteworthy that the LSTM performs even better than the reference data at high latitudes.

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“…O modelo ARMA (do inglês, AutoRegressive Moving Average) é um modelo matemático utilizado para predic ¸ão de séries temporais de natureza estacionária [7] [8]. A ideia principal de realizar a predic ¸ão de séries temporais baseado em modelo ARMA é inicialmente obter um modelo de trei-namento para um conjunto de dados previamente coletados e, depois prever t passos a frente as próximas observac ¸ões temporais baseada no modelo de treinamento obtido.…”

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Predição de Séries Temporais Caóticas baseada em Modelo ARMA via Algoritmo Adaptativo FVSS-NLMS

Noronha¹

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Neste trabalho é utilizado o modelo ARMA (do inglês, AutoRegressive Moving Average) para a predição de séries temporais caóticas. A estimativa do vetor de pesos do modelo ARMA pode ser obtida através de um algoritmo de otimização baseado em gradiente descendente estocástico, de tal forma que o desempenho de predição de uma série temporal caótica realizada pelo modelo ARMA é influenciado pelo valor do tamanho de passo. Para isto, neste trabalho é proposta uma nova versão do algoritmo NLMS (do inglês, Normalized Least Mean Square) com o tamanho de passo adaptado por um MFIS (do inglês, Mamdani Fuzzy Inference System) para atualização da estimativa do vetor de pesos do modelo ARMA. O desempenho do modelo ARMA para a predição de séries temporais caóticas foi comparado com os resultados obtidos pelas versões tradicionais dos algoritmos LMS (do inglês, Least Mean Square) e NLMS.

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Linear time-series modeling of the GNSS based TEC variations over Southwest Japan during 2011–2018 and comparison against ARMA and GIM models

Cited by 35 publications

References 47 publications

Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014–2019 and the 2016 Kumamoto Earthquake

Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014–2019 and the 2016 Kumamoto Earthquake

LSTM-based short-term ionospheric TEC forecast model and position accuracy analysis

Predição de Séries Temporais Caóticas baseada em Modelo ARMA via Algoritmo Adaptativo FVSS-NLMS

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