A zenith wet delay improved model in China based on GPT3 and random forest
Zenith wet delay (ZWD) is a key parameter for the precise positioning of global navigation satellite systems (GNSS) and occupies a central role in meteorological research. Currently, most models only consider the periodic variability of the ZWD, neglecting the effect of nonlinear factors on the ZWD...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-07-01
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| Series: | Geodesy and Geodynamics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674984724001198 |
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| author | Shaoni Chen Chunhua Jiang Xiang Gao Huizhong Zhu Shuaimin Wang Guangsheng Liu |
| author_facet | Shaoni Chen Chunhua Jiang Xiang Gao Huizhong Zhu Shuaimin Wang Guangsheng Liu |
| author_sort | Shaoni Chen |
| collection | DOAJ |
| description | Zenith wet delay (ZWD) is a key parameter for the precise positioning of global navigation satellite systems (GNSS) and occupies a central role in meteorological research. Currently, most models only consider the periodic variability of the ZWD, neglecting the effect of nonlinear factors on the ZWD estimation. This oversight results in a limited capability to reflect the rapid fluctuations of the ZWD. To more accurately capture and predict complicated variations in ZWD, this paper developed the CRZWD model by a combination of the GPT3 model and random forests (RF) algorithm using 5-year atmospheric profiles from 70 radiosonde (RS) stations across China. Taking the external 25 test stations data as reference, the root mean square (RMS) of the CRZWD model is 29.95 mm. Compared with the GPT3 model and another model using backpropagation neural network (BPNN), the accuracy has improved by 24.7% and 15.9%, respectively. Notably, over 56% of the test stations exhibit an improvement of more than 20% in contrast to GPT3-ZWD. Further temporal and spatial characteristic analyses also demonstrate the significant accuracy and stability advantages of the CRZWD model, indicating the potential prospects for GNSS-based applications. |
| format | Article |
| id | doaj-art-d2502deb3c3a4f5d96381e7ff51eb7b6 |
| institution | Kabale University |
| issn | 1674-9847 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Geodesy and Geodynamics |
| spelling | doaj-art-d2502deb3c3a4f5d96381e7ff51eb7b62025-08-20T03:24:59ZengKeAi Communications Co., Ltd.Geodesy and Geodynamics1674-98472025-07-0116440341210.1016/j.geog.2024.11.003A zenith wet delay improved model in China based on GPT3 and random forestShaoni Chen0Chunhua Jiang1Xiang Gao2Huizhong Zhu3Shuaimin Wang4Guangsheng Liu5School of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin 123000, China; State Key Laboratory of Geo-Information Engineering, Xi’an 710054, China; State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China; Corresponding author. School of Geomatics, Liaoning Technical University, Fuxin, 123000, China.School of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaCollege of Mining and Geomatics, Hebei University of Engineering, Handan 056038, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaZenith wet delay (ZWD) is a key parameter for the precise positioning of global navigation satellite systems (GNSS) and occupies a central role in meteorological research. Currently, most models only consider the periodic variability of the ZWD, neglecting the effect of nonlinear factors on the ZWD estimation. This oversight results in a limited capability to reflect the rapid fluctuations of the ZWD. To more accurately capture and predict complicated variations in ZWD, this paper developed the CRZWD model by a combination of the GPT3 model and random forests (RF) algorithm using 5-year atmospheric profiles from 70 radiosonde (RS) stations across China. Taking the external 25 test stations data as reference, the root mean square (RMS) of the CRZWD model is 29.95 mm. Compared with the GPT3 model and another model using backpropagation neural network (BPNN), the accuracy has improved by 24.7% and 15.9%, respectively. Notably, over 56% of the test stations exhibit an improvement of more than 20% in contrast to GPT3-ZWD. Further temporal and spatial characteristic analyses also demonstrate the significant accuracy and stability advantages of the CRZWD model, indicating the potential prospects for GNSS-based applications.http://www.sciencedirect.com/science/article/pii/S1674984724001198Zenith wet delayCRZWD modelGPT3Random forestBack propagation neural network |
| spellingShingle | Shaoni Chen Chunhua Jiang Xiang Gao Huizhong Zhu Shuaimin Wang Guangsheng Liu A zenith wet delay improved model in China based on GPT3 and random forest Geodesy and Geodynamics Zenith wet delay CRZWD model GPT3 Random forest Back propagation neural network |
| title | A zenith wet delay improved model in China based on GPT3 and random forest |
| title_full | A zenith wet delay improved model in China based on GPT3 and random forest |
| title_fullStr | A zenith wet delay improved model in China based on GPT3 and random forest |
| title_full_unstemmed | A zenith wet delay improved model in China based on GPT3 and random forest |
| title_short | A zenith wet delay improved model in China based on GPT3 and random forest |
| title_sort | zenith wet delay improved model in china based on gpt3 and random forest |
| topic | Zenith wet delay CRZWD model GPT3 Random forest Back propagation neural network |
| url | http://www.sciencedirect.com/science/article/pii/S1674984724001198 |
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