Performance evaluation of neural network TEC forecasting models over equatorial low-latitude Indian GNSS station

Sivavaraprasad, G.; Deepika, V.S.; Rao, D. Sreenivasa; Kumar, M. Ravi; Sridhar, M.

doi:10.1016/j.geog.2019.11.002

Cited by 30 publications

(13 citation statements)

References 24 publications

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“…7). The ML models have been compared with several conventional non-ML models: regression model [14,80,101,159,160], brute force approach [143], traditional statistical approaches [60,94,[161][162][163][164], classical KF [129], Bayes-optimal rule [118], least square (LS)-based approach [40], Saastamoinen model [110], autoregressive model and a traditional LEO propagation model (EKF-STAN) [146], conventional wind speed retrieval method [43], Maximum-Likelihood Power-Distortion (PD-ML) [165], BERNESE 5.2 [114], CYGNSS [44], Hydrostaticseasonal-time (HST) model [49], Statistical Theta method [51][52][53]166], MAPGEO2004 geoid model [73], GNSS-IR soil moisture [58], Autoregressive (AR) and Autoregressive Moving Average (ARMA) [167], ERA-Interima global atmospheric reanalysis (now ERA5 reanalysis) [107], Empirical linear algorithms (LRM and LLM) [59], International Reference Ionosphere (IRI) 2016 model [168], NeQuick and IRI-2001 global TEC model [169][170][171], EKF-based integration scheme [172], CODE GIMs (Global Ionospheric Maps) [173], autoregressive integrated moving average (ARIMA), and quadratic polynomial (QP) models [174], least square regression algorithms (LSR) and bi-ha...…”

Section: E ML Vs Non-ml Models (Rq4a)mentioning

confidence: 99%

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Siemuri

Selvan

Välisuo

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order to improve their robustness and performance in less than optimal signal environments, many researchers have proposed machine learning (ML) based GNSS models (ML models) as early as the 1990s. However, no study has been done in a systematic way to analyze the extent of the research on the utilization of ML models in GNSS and their performance. The aim of this research is to perform a systematic review of the type of ML models utilized in GNSS use cases, their performance with respect to accuracy, their comparison with other models (ML and non-ML), and their GNSS application context. In this study, we perform a systematic review of studies from 2000 to 2021 in the literature that utilizes machine learning techniques in GNSS use cases. We assess the performance of the machine learning techniques in the existing literature on their application to GNSS. Furthermore, the strengths and weaknesses of machine learning techniques are summarized. In this paper, we have identified 213 selected studies and ten categories of machine learning techniques. The results prove the acceptable performance of machine learning

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Section: E ML Vs Non-ml Models (Rq4a)mentioning

confidence: 99%

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Siemuri

Selvan

Välisuo

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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show abstract

“…Artificial neural networks (ANN) can model the system with less information. With less or missing ionospheric TEC observation data, ANN can effectively establish TEC prediction models and complete TEC maps (Sivavaraprasad et al., 2020).…”

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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“…The ionosphere is an important region of earth space that is coupled upward with the magnetosphere and influenced downward by the lower atmosphere [7]. The ionosphere is also affected by solar activity and geomagnetic activity; thus, the ionosphere has very complex temporal and spatial variations [8]. With the increase in human space activities, the demand for monitoring and forecasting the ionospheric space environment is increasing.…”

Section: Introductionmentioning

confidence: 99%

Ionospheric TEC Prediction Base on Attentional BiGRU

Lei

Liu

et al. 2022

Atmosphere

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Many studies indicated that ionospheric total electron content (TEC) prediction is vital for terrestrial and space-based radio-communication systems. In previous TEC prediction schemes based on RNN, they learn TEC representations from previous time steps, and each time-step made an equal contribution to a prediction. To overcome these drawbacks, we propose two improvements in our study: (1) To predict TEC with both past and future time-step, Bidirectional Gate Recurrent Unit (BiGRU) was presented to improve the capabilities. (2) To highlight critical time-step information, attention mechanism was used to provide weights to each time-step. The proposed attentional BiGRU TEC predicting method was evaluated on the publicly available data set from the Centre for Orbit Determination in Europe. We chose three geographical locations in low latitude, middle latitude, and high latitude to verify the performance of our proposed model. Comparative experiments were conducted using Deep Neural Network (DNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Term memory (BiLSTM), and Gated Recurrent Unit (GRU). Experimental results show that the proposed Attentional BiGRU model is superior to the other models in the selected nine regions. In addition, the paper discussed the effects of latitudes and solar activities on the performance of Attentional BiGRU model. Experimental results show that the higher the latitude, the higher the prediction accuracy of our proposed model. Experimental results also show that in the middle latitude, the prediction accuracy of the model is less affected by solar activity, and in other areas, the model is greatly affected by solar activity.

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Performance evaluation of neural network TEC forecasting models over equatorial low-latitude Indian GNSS station

Cited by 30 publications

References 24 publications

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

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

Ionospheric TEC Prediction Base on Attentional BiGRU

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