A comparative study for the estimation of geodetic

Authors: M YILMAZ M GULLU

Publish Date: 2014/06/17

Volume: 123, Issue: 4, Pages: 791-808

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Abstract

Space geodesy era provides velocity information which results in the positioning of geodetic points by considering the time evolution The geodetic point positions on the Earth’s surface change over time due to plate tectonics and these changes have to be accounted for geodetic purposes The velocity field of geodetic network is determined from GPS sessions Velocities of the new structured geodetic points within the geodetic network are estimated from this velocity field by the interpolation methods In this study the utility of Artificial Neural Networks ANN widely applied in diverse fields of science is investigated in order to estimate the geodetic point velocities Back Propagation Artificial Neural Network BPANN and Radial Basis Function Neural Network RBFNN are used to estimate the geodetic point velocities In order to evaluate the performance of ANNs the velocities are also interpolated by Kriging KRIG method The results are compared in terms of the root mean square error RMSE over five different geodetic networks It was concluded that the estimation of geodetic point velocity by BPANN is more effective and accurate than by KRIG when the points to be estimated are more than the points knownThis study was supported by Afyon Kocatepe University Scientific Research Projects Coordination Department Project No 11FENBIL23 The authors thank the two anonymous reviewers whose constructive comments led to significant improvements on the original manuscript

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