Abstract

Nowadays  the  GPS  measurements  are  one  of  the  most frequently  used technique  in  geodesy. With this technique ellipsoidal height can be reckoned. However in the engineering practice orthometric heights (height above sea level) are used.  The orthometric heights are determined by levelling.  Transforming the GPS-derived ellipsoidal heights to orthometric heights it is important to know the distance between the ellipsoidal and the geoid surface, called the geoid height or geoid undulation. GPS levelling method is easy to determine geoid height of related region. Geoid height calculated by soft computing methods such as fuzzy logic and neural networks has gained more popularity recently. In this study, it examined effect of increasing number of neurons in neural networks to determine geoid height. The  neural  network  approach  used  in  this  study  is based on  a back propagation neural network  learning the functional  relationship  between  geographic  position and  geoid undulation.  Thus, inputs to the neural network are geographic position (latitude and longitude), and the output from the network is the predicted geoid undulation.

Key words: Geoid height, GPS, Neural networks, neuron.