Performance evaluation of neural network TEC forecasting models over equatorial low-latitude Indian GNSS station
TLDR
In this article, the performance of TEC forecasting models based on Neural Networks (NN) have been evaluated to forecast (1-h ahead) ionospheric TEC over equatorial low latitude Bengaluru (12.97 ∘ N, 77.59 ∘ E ), Global Navigation Satellite System (GNSS) station, India.About:
This article is published in Geodesy and Geodynamics.The article was published on 2020-05-01 and is currently open access. It has received 25 citations till now. The article focuses on the topics: TEC & Total electron content.read more
Citations
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Implementation of Hybrid Deep Learning Model (LSTM-CNN) for Ionospheric TEC Forecasting Using GPS Data
TL;DR: This letter provides the application of deep learning models, long short-term memory (LSTM), gated recurrent unit (GRU), and a hybrid model that consists of LSTM combined with convolution neural network (CNN) to forecast the ionospheric delays for GPS signals.
Journal ArticleDOI
Long Short-Term Memory and Gated Recurrent Neural Networks to Predict the Ionospheric Vertical total electron Content
Kenneth Iluore,Jianrong Lu +1 more
TL;DR: In this paper , the performance of deep learning models such as Long Short-Term Memory (LSTM) and a recently proposed Gated Recurrent Unit (GRU) in forecasting the ionospheric GPS-VTEC, and compare the performance with that of Multilayer Perceptron (MLP) neural networks, GIM_TEC and the IRI-Plas 2017 models.
Journal ArticleDOI
Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis Approach
TL;DR: In this article, an approach to represent Australian total electron content (TEC) using the spherical cap harmonic analysis (SCHA) and artificial neural network (ANN) is proposed, and the results demonstrate that the ANN-aided SCHA method is an effective approach for mapping and investigating the TEC maps over Australia.
Journal ArticleDOI
Ionospheric TEC prediction using Long Short-Term Memory deep learning network
TL;DR: In this paper, the prediction model for ionospheric total electron content (TEC) based on Long Short-Term Memory (LSTM) deep learning network and its performance are discussed.
Journal ArticleDOI
Application of a Multi-Layer Artificial Neural Network in a 3-D Global Electron Density Model Using the Long-Term Observations of COSMIC, Fengyun-3C, and Digisonde
Wang Li,Wang Li,Dongsheng Zhao,Changyong He,Yi Shen,Andong Hu,Andong Hu,Kefei Zhang,Kefei Zhang +8 more
TL;DR: In this article, the authors developed a completely global ionospheric three-dimensional electron density model based on an artificial neural network, namely ANN-TDD, to predict the horizontal-vertical features of ionosphere electrodynamics.
References
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TL;DR: In this article, a new solar Extreme Ultraviolet (EUV) flux model for aeronomic calulations is presented, which is based on the measured F74113 solar EUV reference spectrum.
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Solar activity variations of nighttime ionospheric peak electron density
Yiding Chen,Libo Liu,Huijun Le +2 more
TL;DR: In this paper, the authors investigated the seasonal and latitudinal differences of the solar activity variation of nighttime NmF2 with F107, and they found that the variation of the recombination processes around the F2-peak also shows seasonal dependence, and field-aligned plasma influx plays an important role in the EIA crest region.
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Solar activity effects of the ionosphere: A brief review
TL;DR: The solar activity dependence of the ionosphere is a key and fundamental issue in ionospheric physics as mentioned in this paper, providing information essential to understanding the variations in ionosphere and its processes.
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