Data Interpolation Accuracy Comparison: Gravity Model Versus Radial Basis Function

TL;DR

This paper compares the accuracy and convergence of the Gravity model and Radial Basis Function methods for mesh-free data interpolation, demonstrating RBF's superior speed and precision through temperature data in Tennessee.

Contribution

It provides a comparative analysis of two mesh-free interpolation methods, highlighting RBF's faster convergence and higher accuracy over the Gravity model.

Findings

RBF converges faster than the Gravity model.

RBF provides more accurate and smoother temperature interpolations.

Temperature contours show RBF's broader data range.

Abstract

In this paper, the accuracy of two mesh-free approximation approaches, the Gravity model and Radial Basis Function, are compared. The two schemes' convergence behaviors prove that RBF is faster and more accurate than the Gravity model. As a case study, the interpolation of temperature at different locations in Tennesse, USA, are compared. Delaunay mesh generation is used to create random points inside and on the border, which data can be incorporated in these locations. 49 MERRA weather stations as used as data sources to provide the temperature at a specific day and hour. The contours of interpolated temperatures provided in the result section assert RBF is a more accurate method than the Gravity model by showing a smoother and broader range of interpolated data.