Implementation of Hybrid Deep Learning Model (LSTM-CNN) for Ionospheric TEC Forecasting Using GPS Data-Reference-Cited by-同舟云学术

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Implementation of Hybrid Deep Learning Model (LSTM-CNN) for Ionospheric TEC Forecasting Using GPS Data

Published:2021-06 Issue:6 Volume:18 Page:1004-1008
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Ruwali Adarsha^ORCID,Kumar A. J. Sravan^ORCID,Prakash Kolla Bhanu^ORCID,Sivavaraprasad G.,Ratnam D. Venkata^ORCID

Funder

Science and Engineering Research Board, Government of India, Early Career Research Award (ECRA) Scheme

Department of Science and Technology, New Delhi, India, through the Fund for Improvement of S&T Infrastructure (FIST) Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/9438971/09093827.pdf?arnumber=9093827

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2. Prediction of Ionospheric Scintillation with ConvGRU Networks Using GNSS Ground-Based Data across South America;Remote Sensing;2024-07-28

3. MAOOA‐Residual‐Attention‐BiConvLSTM: An Automated Deep Learning Framework for Global TEC Map Prediction;Space Weather;2024-07

4. A hybrid model for TEC prediction using BiLSTM and PSO-LSSVM;Advances in Space Research;2024-07

5. A Comparative Study on Multi-Parameter Ionospheric Disturbances Associated with the 2015 Mw 7.5 and 2023 Mw 6.3 Earthquakes in Afghanistan;Remote Sensing;2024-05-22

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