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.
翻译:在本文中,比较了两种无网状近似方法(重力模型和辐射基准函数)的准确性。两种办法的趋同行为证明RBF比重力模型更快、更准确。作为案例研究,比较了美国Tennesse不同地点的温度的内插。Delaunay网状生成被用于在边界内外建立随机点,这些数据可以纳入这些地点。49个MERRA气象站作为数据源,用于提供特定日、小时的温度。结果部分提供的内插温度轮廓表明RBF是一个比重力模型更准确的方法,通过显示更平滑、范围更广的内插数据。