Local TEC modelling and forecasting using neural networks
TLDR
This paper presents modelling efforts of TEC taking into account solar and geomagnetic activity, time of the day and day of the year using neural networks (NNs) modelling technique and finds that NN model performs better than the corresponding NeQuick 2 model for low latitude region.About:
This article is published in Journal of Atmospheric and Solar-Terrestrial Physics.The article was published on 2018-07-01 and is currently open access. It has received 28 citations till now. The article focuses on the topics: TEC.read more
Citations
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Journal ArticleDOI
Long Short-Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China
Journal ArticleDOI
Modeling and predicting seasonal ionospheric variations in Turkey using artificial neural network (ANN)
Samed Inyurt,Aliihsan Sekertekin +1 more
TL;DR: In this paper, an artificial neural network (ANN) was used to model and predict seasonal ionospheric total electron content (TEC) using GPS observations acquired from ANKR GPS station (Turkey) in 2015.
Journal ArticleDOI
Feed forward neural network based ionospheric model for the East African region
TL;DR: In this paper, a neural network based regional ionospheric model is developed using GPS-TEC data from 1 January 2012 to 31 December 2015, which can capture most of the spatio-temporal variations of the regional TEC.
Journal ArticleDOI
Ionospheric TEC forecast model based on support vector machine with GPU acceleration in the China region
Guozhen Xia,Yi Liu,Tongfeng Wei,Zhuangkai Wang,Weiquan Huang,Zhitao Du,Zhibiao Zhang,Xiang Wang,Chen Zhou +8 more
TL;DR: In this paper, support vector machine (SVM) with GPU acceleration was used for developing a regional forecast model for the ionospheric total electron content (TEC) over China region.
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.
References
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