Assessing Interpolation Methods for Accuracy of Design Groundwater Levels for Civil Projects

This research compared natural neighbor interpolation with other interpolation methods commonly implemented in ArcGIS. It evaluated the relative performance of interpolation methods for various spatial data distributions, including line transects. It characterized locations which are associated with large prediction errors. To assess the relative performance of interpolation methods, a validation procedure was used consisting of 75% training data and 25% test data. Statistical error measures were used to measure the predictive performance of the interpolation methods, and the spatial distribution of errors was used to characterize areas where interpolation methods performed poorly. Results showed that Topo to Raster, natural neighbor, ordinary kriging, and empirical Bayesian kriging methods consistently outperformed other interpolation methods for a variety of spatial distributions of the data. However, natural neighbor interpolation was unsuitable for linear transects. In general, the accuracy of most of the interpolation methods increased with narrow spatial data distributions. Spatial distribution of large prediction errors was predominantly similar, regardless of the interpolation method used, and was related to changes in physical characteristics.