首页|期刊导航|大地测量与地球动力学（英文版）|Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithm
大地测量与地球动力学（英文版）2026，Vol.17Issue(2)：211-224,14.DOI:10.1016/j.geog.2025.06.004
Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithm
Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithm
摘要
关键词
Tropospheric zenith wet delay/Machine learning/Extra trees/Machine learning fusion algorithm/Empirical modelsKey words
Tropospheric zenith wet delay/Machine learning/Extra trees/Machine learning fusion algorithm/Empirical models引用本文复制引用
Jiahao Zhang,Qin Liang,Yunqing Huang..Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithm[J].大地测量与地球动力学（英文版）,2026,17(2):211-224,14.基金项目
The authors would like to thank the National Centers for Environmental Information/National Oceanic and Atmospheric Administration(NCEI/NOAA)for providing Integrated Global Radiosonde Archive(IGRA)v2 radiosonde data(https://www.ncei.noaa.gov/pub/data/igra/),and the research group of Advanced Geodesy of TU Vienna for providing GPT3 model(https://vmf.geo.tuwien.ac.at/codes/).This research was funded by National Natural Science Foundation of China Key Program(12431014),Key Project of Hunan Education Department(22A0126),Natural Science Foundation of Hunan Province(2022JJ30555)and Postgraduate Scientific Research Innovation Project of Xiangtan University(XDCX2024Y172). （NCEI/NOAA）
