Articles citing this article

The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).

Cited article:

Prediction of ionospheric total electron content data using spatio-temporal residual network

Nayana Shenvi, E. Chandrasekhar, Anurag Kumar and Hassanali Virani
Advances in Space Research 72 (11) 4856 (2023)
https://doi.org/10.1016/j.asr.2023.09.006

Improving IRI-2016 global total electron content maps using ELM neural network

Masoud Dehvari, Sedigheh Karimi, Saeed Farzaneh and Mohammad Ali Sharifi
Advances in Space Research 72 (9) 3903 (2023)
https://doi.org/10.1016/j.asr.2023.07.022

Performance of short-terms prediction methods of vertical total electron content using nonlinear autoregressive neuronal network and stochastic autoregressive model

M. Paula Natali and Amalia Meza
Advances in Space Research 72 (9) 3919 (2023)
https://doi.org/10.1016/j.asr.2023.07.035

A Novel Approach for Establishing the Global Ionospheric Model With High Spatiotemporal Resolution

Peng Chen, Yuchen Zhang, Rong Wang, Zhiyuan An and Yibin Yao
IEEE Transactions on Geoscience and Remote Sensing 61 1 (2023)
https://doi.org/10.1109/TGRS.2023.3238044

Ionosphere variability II: Advances in theory and modeling

Ioanna Tsagouri, David R. Themens, Anna Belehaki, Ja-Soon Shim, Mainul M. Hoque, Grzegorz Nykiel, Claudia Borries, Anna Morozova, Teresa Barata and Wojciech J. Miloch
Advances in Space Research (2023)
https://doi.org/10.1016/j.asr.2023.07.056

Ionospheric Weather at Two Starlink Launches during Two-Phase Geomagnetic Storms

Tamara Gulyaeva, Manuel Hernández-Pajares and Iwona Stanislawska
Sensors 23 (15) 7005 (2023)
https://doi.org/10.3390/s23157005

Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region

Artem Kharakhashyan and Olga Maltseva
Remote Sensing 15 (12) 3069 (2023)
https://doi.org/10.3390/rs15123069

Using Deep Learning to Map Ionospheric Total Electron Content over Brazil

Andre Silva, Alison Moraes, Jonas Sousasantos, et al.
Remote Sensing 15 (2) 412 (2023)
https://doi.org/10.3390/rs15020412

An improved NeQuick-G global ionospheric TEC model with a machine learning approach

K. Sivakrishna, D. Venkata Ratnam and Gampala Sivavaraprasad
GPS Solutions 27 (2) (2023)
https://doi.org/10.1007/s10291-023-01426-4

Collecting volunteered geographic information from the Global Navigation Satellite System (GNSS): experiences from the CAMALIOT project

Linda See, Benedikt Soja, Grzegorz Kłopotek, Tobias Sturn, Rudi Weinacker, Santosh Karanam, Ivelina Georgieva, Yuanxin Pan, Laura Crocetti, Markus Rothacher, Vicente Navarro, Steffen Fritz and Ian McCallum
International Journal of Digital Earth 16 (1) 2818 (2023)
https://doi.org/10.1080/17538947.2023.2239761

CARMEN 2 and 3 LEO Electron Flux Measurements Linear Projection Onto RBSP Elliptical Orbit

François Ginisty, Frédéric Wrobel, Robert Ecoffet, Denis Standarovski, Julien Mekki, Marine Ruffenach, Nicolas Balcon and Alain Michez
IEEE Transactions on Nuclear Science 70 (8) 1564 (2023)
https://doi.org/10.1109/TNS.2023.3260904

Advances in Geospatial Technology in Mining and Earth Sciences

Nhung Le, Benjamin Männel, Luyen K. Bui, et al.
Environmental Science and Engineering, Advances in Geospatial Technology in Mining and Earth Sciences 137 (2023)
https://doi.org/10.1007/978-3-031-20463-0_9

Regional modeling and forecasting of precipitable water vapor using least square support vector regression

Seyyed Reza Ghaffari-Razin, Reza Davari Majd and Navid Hooshangi
Advances in Space Research 71 (11) 4725 (2023)
https://doi.org/10.1016/j.asr.2023.01.030

Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data

Shuangshuang Shi, Kefei Zhang, Jiaqi Shi, Andong Hu, Dongsheng Zhao, Zhongchao Shi, Peng Sun, Huajing Wu and Suqin Wu
Space Weather 21 (4) (2023)
https://doi.org/10.1029/2022SW003357

Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)

Zhou Chen, Bokun An, Wenti Liao, et al.
Atmosphere 14 (5) 810 (2023)
https://doi.org/10.3390/atmos14050810

Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting

Yuhuan Yuan, Guozhen Xia, Xinmiao Zhang and Chen Zhou
Space Weather 21 (10) (2023)
https://doi.org/10.1029/2023SW003472

Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set

Zihan Wang, Shasha Zou, Hu Sun and Yang Chen
Space Weather 21 (8) (2023)
https://doi.org/10.1029/2023SW003494

Bi-LSTM based vertical total electron content prediction at low-latitude equatorial ionization anomaly region of South India

Veera Kumar Maheswaran, James A. Baskaradas, Raju Nagarajan, Rajesh Anbazhagan, Sriram Subramanian, Venkata Ratnam Devanaboyina and Rupesh M. Das
Advances in Space Research (2023)
https://doi.org/10.1016/j.asr.2023.08.054

Support Vector Regression model to predict TEC for GNSS signals

Kondaveeti Sivakrishna, Devanaboyina Venkata Ratnam and Gampala Sivavaraprasad
Acta Geophysica 70 (6) 2827 (2022)
https://doi.org/10.1007/s11600-022-00954-w

Potential Impact of GNSS Positioning Errors on the Satellite‐Navigation‐Based Air Traffic Management

Dabin Xue, Jian Yang and Zhizhao Liu
Space Weather 20 (7) (2022)
https://doi.org/10.1029/2022SW003144

Aeronomic and Dynamic Correction of the Global Model GTEC for Disturbed Conditions

V. N. Shubin, T. L. Gulyaeva and M. G. Deminov
Geomagnetism and Aeronomy 62 (S1) S74 (2022)
https://doi.org/10.1134/S0016793222600667

Ionospheric TEC Forecasting over an Indian Low Latitude Location Using Long Short-Term Memory (LSTM) Deep Learning Network

Kanaka Durga Reddybattula, Likhita Sai Nelapudi, Mefe Moses, et al.
Universe 8 (11) 562 (2022)
https://doi.org/10.3390/universe8110562

Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods

Mir-Reza Ghaffari Razin and Behzad Voosoghi
Advances in Space Research 69 (7) 2671 (2022)
https://doi.org/10.1016/j.asr.2022.01.003

The Variation Characteristics and Prediction Performance of TEC in the Geomagnetic Latitude and Local Time Coordinate

Simin Zhang, Xiaocheng Wu and Xiong Hu
Radio Science 57 (12) (2022)
https://doi.org/10.1029/2022RS007544

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

Shuangshuang Shi, Kefei Zhang, Suqin Wu, Jiaqi Shi, Andong Hu, Huajing Wu and Yu Li
Space Weather 20 (6) (2022)
https://doi.org/10.1029/2022SW003103

Generation of Proxy GIM‐TEC for Extreme Storms Before the Era of GNSS Observations

Tamara Gulyaeva, Valentin Shubin, Haris Haralambous, Manuel Hernández‐Pajares and Iwona Stanislawska
Journal of Geophysical Research: Space Physics 127 (1) (2022)
https://doi.org/10.1029/2021JA029846

A novel hybrid Machine learning model to forecast ionospheric TEC over Low-latitude GNSS stations

G. Sivavaraprasad, I. Lakshmi Mallika, K. Sivakrishna and D. Venkata Ratnam
Advances in Space Research 69 (3) 1366 (2022)
https://doi.org/10.1016/j.asr.2021.11.033

Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting

Randa Natras, Benedikt Soja and Michael Schmidt
Remote Sensing 14 (15) 3547 (2022)
https://doi.org/10.3390/rs14153547

Machine Learning Methods Applied to the Global Modeling of Event-Driven Pitch Angle Diffusion Coefficients During High Speed Streams

G. Kluth , J.-F. Ripoll , S. Has , et al.
Frontiers in Physics 10 (2022)
https://doi.org/10.3389/fphy.2022.786639

Convolutional Neural Networks for Automated ULF Wave Classification in Swarm Time Series

Alexandra Antonopoulou, Georgios Balasis, Constantinos Papadimitriou, et al.
Atmosphere 13 (9) 1488 (2022)
https://doi.org/10.3390/atmos13091488

Modeling and forecasting of ionosphere TEC using least squares SVM in central Europe

Seyyed Reza Ghaffari-Razin, Amir Reza Moradi and Navid Hooshangi
Advances in Space Research 70 (7) 2035 (2022)
https://doi.org/10.1016/j.asr.2022.06.020

ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model

Guozhen Xia, Fubin Zhang, Cheng Wang and Chen Zhou
Space Weather 20 (8) (2022)
https://doi.org/10.1029/2021SW002959

One‐Day Forecasting of Global TEC Using a Novel Deep Learning Model

Sujin Lee, Eun‐Young Ji, Yong‐Jae Moon and Eunsu Park
Space Weather 19 (1) (2021)
https://doi.org/10.1029/2020SW002600

GIMLi: Global Ionospheric total electron content model based on machine learning

Aleksei V. Zhukov, Yury V. Yasyukevich and Aleksei E. Bykov
GPS Solutions 25 (1) (2021)
https://doi.org/10.1007/s10291-020-01055-1

Prediction of Ionospheric TEC Based on the NARX Neural Network

Liu Guoyan, Gao Wang, Zhang Zhengxie, Zhao Qing and Chao Hu
Mathematical Problems in Engineering 2021 1 (2021)
https://doi.org/10.1155/2021/7188771

A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly

Marjolijn Adolfs and Mohammed Mainul Hoque
Remote Sensing 13 (22) 4559 (2021)
https://doi.org/10.3390/rs13224559

Space Weather Services for Civil Aviation—Challenges and Solutions

Kirsti Kauristie, Jesse Andries, Peter Beck, et al.
Remote Sensing 13 (18) 3685 (2021)
https://doi.org/10.3390/rs13183685

Predicting the Effects of Solar Storms on the Ionosphere Based on a Comparison of Real-Time Solar Wind Data with the Best-Fitting Historical Storm Event

Erik Schmölter and Jens Berdermann
Atmosphere 12 (12) 1684 (2021)
https://doi.org/10.3390/atmos12121684

Space Weather research in the Digital Age and across the full data lifecycle: Introduction to the Topical Issue

Ryan M. McGranaghan, Enrico Camporeale, Manolis Georgoulis and Anastasios Anastasiadis
Journal of Space Weather and Space Climate 11 50 (2021)
https://doi.org/10.1051/swsc/2021037

Prediction of TEC using NavIC/GPS data with geostatistical method/forecasting capability comparison with other models

R. Mukesh, V. Karthikeyan, P. Soma and P. Sindhu
Astrophysics and Space Science 365 (9) (2020)
https://doi.org/10.1007/s10509-020-03868-5