The WOA–CNN–LSTM–Attention Model for Predicting GNSS Water Vapor-Reference-Cited by-同舟云学术

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The WOA–CNN–LSTM–Attention Model for Predicting GNSS Water Vapor

Published:2024 Issue: Volume:62 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Yan Xiangrong¹^ORCID,Yang Weifang¹^ORCID,Gao Motong¹,Ding Nan²^ORCID,Zhang Wenyuan³^ORCID,Li Longjiang³,Hou Yuhao¹,Zhang Kefei³^ORCID

Affiliation:

1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, China

2. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, China

3. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China

Funder

National Natural Science Foundation of China

Lanzhou Jiaotong University

China-Poland Internal Cooperation Programme

International Cooperation Projects in Jiangsu Province, China-Japan Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10540630.pdf?arnumber=10540630

Reference43 articles.

1. GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system

2. Anthropogenic greenhouse forcing and strong water vapor feedback increase temperature in Europe

3. Near real-time GPS sensing of atmospheric water vapor

4. Transport of Atmospheric Moisture during Three Extreme Rainfall Events over the Mackenzie River Basin

5. Research progress and prospect of GNSS space environment science;Yao;Acta Geophys. Sin.,2017

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