USING A LEAST SQUARES SUPPORT VECTOR MACHINE TO ESTIMATE A LOCAL GEOMETRIC GEOID MODEL

Autores

  • SZU-PYNG KAO KAO National Chung Hsing University
  • CHAO-NAN CHEN National Chung Hsing University
  • HUI-CHI HUANG National Chung Hsing University
  • YU-TING SHEN National Chung Hsing University

DOI:

https://doi.org/10.5380/bcg.v20i2.36752

Palavras-chave:

Least Squares Support Vector Machine (LS-SVM), Kernel Function, Local Geoid Model

Resumo

In this study, test-region global positioning system (GPS) control points exhibiting
known first-order orthometric heights were employed to obtain the points of plane
coordinates and ellipsoidal heights by using the real-time GPS kinematic
measurement method. Plane-fitting, second-order curve-surface fitting, back-
propagation (BP) neural networks, and least-squares support vector machine (LS-
SVM) calculation methods were employed. The study includes a discussion on data
integrity and localization, changing reference-point quantities and distributions to
obtain an optimal solution. Furthermore, the LS-SVM was combined with local
geoidal-undulation models that were established by researching and analyzing3
kernel functions. The results indicated that the overall precision of the local
geometric geoidal-undulation values calculated using the radial basis function
(RBF) and third-order polynomial kernel function was optimal and the root mean
square error (RMSE) was approximately ± 1.5 cm. These findings demonstrated that
the LS-SVM provides a rapid and practical method for determining orthometric
heights and should serve as a valuable academic reference regarding local geoid
models.

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Publicado

2014-06-06

Como Citar

KAO, S.-P. K., CHEN, C.-N., HUANG, H.-C., & SHEN, Y.-T. (2014). USING A LEAST SQUARES SUPPORT VECTOR MACHINE TO ESTIMATE A LOCAL GEOMETRIC GEOID MODEL. Boletim De Ciências Geodésicas, 20(2). https://doi.org/10.5380/bcg.v20i2.36752

Edição

v. 20 n. 2 (2014)

Seção

Artigos

Licença

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