Combining Model for Regional GPS Height Conversion Based on Least Squares Support Vector Machines

In geographical information engineering, height anomalies must be known in order to convert GPS ellipsoid heights into geodetic heights. There are many conversion models, such as polynomial, BP neural network and multi-quadrics fitting. Because the quasi-geoid is an irregular geometric object, every method has both advantages and disadvantages, and is appropriate to different conversion patterns. It is difficult to identify which conversion model is most suitable for a particular area. In order to obtain a more precise and reliable analytical result, the combined model based on least squares support vector machines (LSSVM) has been approached. Derived from statistical learning theory, LSSVM are learning systems that use a hypothesis space of linear function in a high dimensional feature space, trained with a learning algorithm from optimizations theory. As a result, the fitting ability of single models is significantly improved. The combined model still possesses the same important features as the single models. Examples are presented and the results are analyzed in detail to demonstrate the efficiency of the proposed methodology.