Data Interpolation Accuracy Comparison: Gravity Model Versus Radial Basis Function
This work addresses interpolation accuracy for environmental data analysis, but it is incremental as it compares existing methods without introducing new techniques.
The paper compared the accuracy of two mesh-free interpolation methods, the Gravity model and Radial Basis Function (RBF), for temperature data in Tennessee, finding that RBF is faster and more accurate, with results showing smoother and broader interpolated temperature contours.
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.