The Influence of Interpolation Methods and point density on the Accuracy of a Bathymetric Model

The construction of three-dimensional models of the seabed starting from sample data requires the use of interpolators to calculate the depth where it has not been measured. The accuracy of the model depends on several factors, e.g., the interpolation method, the seabed morphology, the density and distribution of the samples. This article aims to investigate the accuracy of bathymetric models in relation to the interpolation methods and the number of points available. Eight different methods available in ArcGIS software are analyzed in this study, including 6 deterministic methods, i.e., Inverse distance weighting (IDW), and 5 variations of Radial Basis Functions (RBFs). Additionally, two stochastic methods, such as Universal Kriging (UK) and Ordinary Kriging (OK), are also examined. The experiments are carried out using the bathymetric information from an Electronic Navigational Chart (ENC) at a scale 1:30,000 concerning the north-eastern sector of the Gulf of Naples. The 12,638 depth points including in the ENC are organized in four datasets presenting different data density (25%, 50%, 75% and 100% of the available data respectively). The results of the study confirm that the accuracy of the models improves as the number of points used increases. Specifically, RBF interpolators are found to be more effective than other methods at low density values (25% and 50% of available data) while Kriging interpolators outperform other methods when using large numbers of points (75% and 100% of available data).