Machine learning approach for GNSS geodetic velocity estimation
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10291_2023_Article_1607.pdf
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8.38 MB
Format
Adobe PDF
Checksum (MD5)
634bad46266329fdbd341d8831256e16
Author(s)
Özarpacı, Seda
Kılıç, Batuhan
Bayrak, Onur C.
Taşkıran, Murat
Doğan, Uğur
Floyd, Michael
Date Issued
January 25, 2024
Publisher
Springer Berlin Heidelberg
Citation
GPS Solutions. 2024 Jan 25;28(2):65
Version
Final published version
Abstract
This study aimed to investigate the performance of machine learning (ML) algorithms in determining horizontal velocity at specific points using the current Global Navigation Satellite System (GNSS) velocity field. To achieve this objective, the analysis utilized the most comprehensive velocity field available for Turkey, where 70% of the GNSS velocities was allocated for training the ML algorithms, while the remaining 30% was used for testing. Contrary to the previous research, the significance of considering the tectonic structure within the study area was emphasized at this point. To determine the tectonic structure of the horizontal velocity field in the region, a preliminary clustering procedure was conducted. Subsequently, distinct ML algorithms were trained using velocity fields associated with different tectonic plates. Moreover, to investigate the impact of the tectonic domain, the entire velocity field was also tested using ML algorithms without considering the tectonic structure. Four different ML algorithms, namely, Gradient Boosting Machines (GBM), LightGBM, Random Forest (RF), and eXtreme Gradient Boosting Machines (XGBoost), were employed to estimate the horizontal velocities (east and north components). The findings imply that incorporating the tectonic structure improved the performance of machine learning predictions, as indicated by the GBM algorithm's decreased root-mean-square error values. In addition, when the tectonic structure was taken into account, the accuracy assessment values for the RF and XGBoost algorithms in the east component decreased significantly. In terms of predicting GNSS velocities, the RF algorithm exhibited the lowest root-mean-square error values compared to other algorithms. The horizontal velocity differences between averages of the reference velocity field and the RF velocity estimates are maximum 0.4 mm/yr.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10291-023-01607-1
