Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithmOA
Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithm
Jiahao Zhang;Qin Liang;Yunqing Huang
School of Mathematics and Computational Science,Xiangtan University,Xiangtan 411105,ChinaSchool of Mathematics and Computational Science,Xiangtan University,Xiangtan 411105,China||National Center for Applied Mathematics in Hunan,Xiangtan 411105,China||Hunan Key Laboratory for Computation and Simulation in Science and Engineering,Xiangtan 411105,ChinaSchool of Mathematics and Computational Science,Xiangtan University,Xiangtan 411105,China||National Center for Applied Mathematics in Hunan,Xiangtan 411105,China||Hunan Key Laboratory for Computation and Simulation in Science and Engineering,Xiangtan 411105,China
Tropospheric zenith wet delayMachine learningExtra treesMachine learning fusion algorithmEmpirical models
Tropospheric zenith wet delayMachine learningExtra treesMachine learning fusion algorithmEmpirical models
《大地测量与地球动力学（英文版）》 2026 (2)
211-224,14
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).
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