Polynomials, radial basis functions and multilayer perceptron neural network methods in local geoid determination with GPS/ levelling

摘要

The means for determining reference surfaces when using satellite-based positioning techniques differs from the way they are determined using classical terrestrial positioning techniques. The height of any point on earth, as determined by a global positioning system (GPS), is based on the World Geodetic System of 1984 datum, but when using classical terrestrial measurements this height is based on the geoid. However, GPS-derived ellipsoidal heights must initially be transformed into orthometric heights for practical applications. We use geoid models developed with geoid heights for the height transformation, because orthometric height determination methods using classical techniques require time and manpower. In this study, the performances of polynomials, radial basis functions (RBFs) and multilayer perceptron (MLP) neural network algorithms used in local geoid surface modelling were evaluated in the study area. Upon analysis of statistical results, the artificial neural network method was observed to give better results than the other two methods.