Local geoid determination using a generalized regression neural network and interpolation methods: A case study in Kars, Turkey

Abstract

This study aimed to determine the most suitable local geoid model based on 641 GNSS/leveling points within the borders of Kars Province in eastern Turkey using the generalized regression neural network (GRNN), weighted average (WA), multiquadric (MQ), inverse multiquadric (IMQ) function, and local polynomial (LP) method. Among these methods used in local geoid determination, the studies conducted with the GRNN method are very limited in the literature. To test the performance of the model, 169 GNSS/leveling points were selected as test data. When selecting the reference and test points, care was taken to ensure that the distribution of the points was homogeneous. The criteria of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were used to assess the accuracy and error rates of the results achieved using the different methods. The analysis showed that the GRNN yielded better results than other interpolation methods (RMSE = 1.215 cm, MAE = 0.467 cm, R2 = 0.99980).