Prediction and interpolation of GNSS vertical time series based on the AdaBoost method considering geophysical effects

被引：0

作者：

Lu, Tieding ^{[1
,2
]}

Li, Zhen ^{[1
]}

机构：

[1] School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang

[2] Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, Nanchang

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2024年 / 53卷 / 06期

基金：

中国国家自然科学基金;

关键词：

adaptive boosting algorithm; geophysical effects; GNSS vertical time series; interpolation; prediction;

D O I：

10.11947/j.AGCS.2024.20230434

中图分类号：

学科分类号：

摘要：

Traditional GNSS vertical time series prediction and interpolation methods only consider time variables and have obvious limitations. This study takes into account the impact of geophysical effects and constructs a regression problem using temperature, atmospheric pressure, polar motion, and GNSS vertical time series data, uses the adaptive boost (AdaBoost) algorithm for modeling. To verify the prediction and interpolation performance of the model, the vertical time series from 4 GNSS stations were selected for analysis. The modeling experiment shows that compared to the Prophet model, the fitting accuracy of AdaBoost model has been improved by 35%. The prediction results indicate that within a 12 month prediction period, the MAE values of the AdaBoost model at four GNSS stations are approximately 4. 0 — 4. 5 mm, and the RMSE values are approximately 5. 0 — 6. 0 mm. The interpolation experiment shows that compared to the cubic spline interpolation method, the accuracy of AdaBoost interpolation model has been improved by about 15% — 28%. Our experiments have shown that the Ada-Boost model considering geophysical effects can be applied to the prediction and interpolation of GNSS vertical time series. © 2024 SinoMaps Press. All rights reserved.

引用

页码：1077 / 1085

页数：8

共 31 条

[21] BAO Zhi, CHANG Guobin, ZHANG Laihong, Et al., Filling missing values of multi-station GNSS coordinate time series based on matrix completion, Measurement, 183, (2021)
[22] LI Zhen, LU Tieding, YU Kegen, Et al., Interpolation of GNSS position time series using GBDT, XGBoost, and RF machine learning algorithms and models error analysis, Remote Sensing, 15, 18, (2023)
[23] JIANG Weiping, LI Zhao, WEI Na, Et al., Progress and thoughts on establishment of geodetic coordinate frame, Acta Geodaetica et Cartographica Sinica, 51, 7, pp. 1259-1270, (2022)
[24] CHEN Jun, LIU Wanzeng, WU Hao, Et al., Smart surveying and mapping: fundamental issues and research agenda[J], Acta Geodaetica et Cartographica Sinica, 50, 8, pp. 995-1005, (2021)
[25] SHI Wenzhong, ZHANG Min, Artificial intelligence for reliable object recognition from remotely sensed data: overall framework design, review and prospect [J], Acta Geodaetica et Cartographica Sinica, 50, 8, pp. 1049-1058, (2021)
[26] CHEN Jun, Al Tinghua, YAN Li, Et al., Hybrid computational paradigm and methods for intelligentized surveying and mappingLJ/OLj, Acta Geodaetica et Cartographica Sinica, pp. 1-19
[27] SCHAPIRE R E., Explaining AdaBoost, Empirical inference, pp. 37-52, (2013)
[28] NATRAS R, SOJA B, SCHMIDT M., Ensemble machine learning of random forest, AdaBoost and XGBoost for vertical total electron content forecasting, Remote Sensing, 14, 15, (2022)
[29] ALTUNTAS C, IBAN M C, SENTURK E, Et al., Machine learning-based snow depth retrieval using GNSS signal-to-noise ratio data, GPS Solutions, 26, 4, (2022)
[30] SUN Wei, ZHU Mingchen, Study on modeling of tropospheric zenith delay in China with BP-AdaBoost strong predictor, Journal of Geodesy and Geodynamics, 42, 1, pp. 35-40, (2022)

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