Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis
The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlati...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/18/4374 |
| _version_ | 1797577317257576448 |
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| author | Zhen Li Tieding Lu Kegen Yu Jie Wang |
| author_facet | Zhen Li Tieding Lu Kegen Yu Jie Wang |
| author_sort | Zhen Li |
| collection | DOAJ |
| description | The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model. |
| first_indexed | 2024-03-10T22:06:33Z |
| format | Article |
| id | doaj.art-9b10e09913e44f46a6cb5f7575729282 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T22:06:33Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9b10e09913e44f46a6cb5f75757292822023-11-19T12:46:48ZengMDPI AGRemote Sensing2072-42922023-09-011518437410.3390/rs15184374Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error AnalysisZhen Li0Tieding Lu1Kegen Yu2Jie Wang3School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, ChinaSchool of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, ChinaSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaThe global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model.https://www.mdpi.com/2072-4292/15/18/4374GNSS position time seriesinterpolationgradient boosting decision treeeXtreme gradient boostingrandom forest |
| spellingShingle | Zhen Li Tieding Lu Kegen Yu Jie Wang Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis Remote Sensing GNSS position time series interpolation gradient boosting decision tree eXtreme gradient boosting random forest |
| title | Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis |
| title_full | Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis |
| title_fullStr | Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis |
| title_full_unstemmed | Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis |
| title_short | Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis |
| title_sort | interpolation of gnss position time series using gbdt xgboost and rf machine learning algorithms and models error analysis |
| topic | GNSS position time series interpolation gradient boosting decision tree eXtreme gradient boosting random forest |
| url | https://www.mdpi.com/2072-4292/15/18/4374 |
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