Comparative analysis of interpolation methods commonly used in bathymetric data generation

This research paper aims to provide a comparative analysis of interpolation methods for estimating bathymetric data. Accurate measurement of bathymetry is crucial for a variety of applications, including navigation, oceanography, and coastal management. However, due to the high cost and difficulty of acquiring bathymetric data, gaps in measurements are common. Interpolation methods are widely used to fill these gaps and estimate the depths of water at unsampled locations. In this study, we analyze the most commonly used interpolation methods, including linear, inverse distance weighting, kriging, triangulated irregular network, moving least squares, piecewise cubic Hermite interpolating polynomial, and spline interpolation. We compare and evaluate the performance of these methods using both simulated and real-world datasets. Additionally, we provide a systematic analysis of the strengths and weaknesses of each method in terms of their accuracy in estimating bathymetry. The goal of this paper is to offer a comprehensive overview of interpolation methods for bathymetric data and to assist researchers and practitioners in selecting the most suitable method for a given task and dataset.