Prediction of Digital Elevation Model Height by Multivariate Adaptive Regression Splines (Mars) Interpolation Approach
The objective of this paper is to assess the applicability and performance of multivariate adoptive regression spline analysis (MARS) for prediction elevation height in digital elevation model. MARS is an adaptive, non-parametric regression approach. Three dimensional co-ordinates (X, Y, and Z) in Equal-Sized grid Cell observed and recognized vie Differential Global Positioning System (DGPS) at AL-Nahrain university site. Mathematical prediction models with their errors and analysis are established in this paper.as the same time the independent variables X,Y and the dependent predicted variable Z the height which be used in . The data were divided randomly into training and testing 70% of the entire data set is utilized for training and the remaining 30% for testing. MARS depends on two steps for computation logarithm forward and backward to get better performance MARS adopts Generalized Cross-Validation (GCV) with different statistical parameters of standard deviation, root mean square error and residuals.
