Abstract
A variety of climate factors influence the precision of the long‐term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long‐term GNSS monitoring records, this study combines the extended seven‐parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local‐scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long‐term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long‐term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long‐term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit‐less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS‐derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long‐term GNSS monitoring.
Highlights
Within the various remote sensing technologies, Global Navigation Satellite Systems (GNSS) plays an important role in providing fundamental infrastructure and has been successfully implemented in deformation monitoring
To precisely analyze the impact of various daily climate factors on the GNSS time series, we proposed a hybrid method and applied it in the Puerto Rico and the Northern Virgin Islands (PRVI) area
We used the extended Helmert transformation method to establish the PRVI19 local reference frame, which could help avoid the bias of background global or regional tectonic movements in the GNSS time series when studying local ground deformation
Summary
Within the various remote sensing technologies, Global Navigation Satellite Systems (GNSS) plays an important role in providing fundamental infrastructure and has been successfully implemented in deformation monitoring. We applied the ensemble learning method to analyze the weights of impact from various daily climate factors on the GNSS monitoring time series. (1) To remove the background tectonic movements when monitoring local ground deformation, we proposed the extended Helmert transformation to establish the highly stable PRVI19 local reference frame based on ten well-distributed continuously operating GNSS stations with at least five years of data (2) By combining the GNSS records with millimeter accuracy and the local climate data with a span of at least five years, we applied the XGboost machine learning algorithm to derive the quantitative results of the weights of impact from different daily climate factors on the GNSS time series (3) Based on the model, we predicted the GNSS records and validate them with the real raw GNSS data. The results show that the high accuracy of the prediction and it is expected that this study can provide a new prospect to explore the potential deformation monitoring problem
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