Comparison of spatial interpolation methods and multi-layer neural networks for different point distributions on a digital elevation model

Abstract

Interpolation of a spatially continuous variable from point samples is an important field in spatial analysis and surface models for geosciences. In this study, spatial interpolation methods which are Inverse Distance Weighted (IDW), Ordinary Kriging (OK), Modified Shepard's (MS), Multiquadric Radial Basis Function (MRBF) and Triangulation with Linear (TWL), and Multi-Layer Perceptron (MLP) which is an Artificial Neural Networks (ANN) method were compared in order to predict height for different point distributions such as curvature, grid, random and uniform on a Digital Elevation Model which is an USGS National Elevation Dataset (NED). This study also aims to quantify the effects of topographic variability and sampling density Errors of different interpolations and ANN prediction were evaluated for different point distributions and three different cross-sections on the characteristic parts of the surface were selected and analyzed. Generally, OK, MS, MRBF and TWL gave promising results and were more effective in terms of characteristics of surface than MLP and IDW. Although MLP simplified the contours obtained from predicted heights, it was a satisfactory predictor for curvature, grid, random and uniform distributions.