Different data-driven prediction of global ionospheric TEC using deep learning methods-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Different data-driven prediction of global ionospheric TEC using deep learning methods

Published:2024-06 Issue: Volume:130 Page:103889
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Tang Jun,Ding Mingfei,Yang Dengpan,Fan Cihang,Khonsari Nasim^ORCID,Mao Wenfei

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference34 articles.

1. A review on remotely sensed land surface temperature anomaly as an earthquake precursor;Bhardwaj;Int. J. Appl. Earth Observ. Geoinform.,2017

2. The international reference ionosphere today and in the future;Bilitza;J. Geod.,2011

3. Random Forests;Breiman;Machine Learning.,2001

4. Study of the ionospheric scintillation radio propagation characteristics with cosmic observations;Chen;Remote Sens. (Basel),2022

5. A short-term prediction model of global ionospheric VTEC based on the combination of long short-term memory and convolutional long short-term memory;Chen;J. Geod.,2023

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP