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arXiv:2102.07890 (cs)
[Submitted on 15 Feb 2021 (v1), last revised 19 Mar 2021 (this version, v2)]

Title:Data Interpolation Accuracy Comparison: Gravity Model Versus Radial Basis Function

Authors:Amirehsan Ghasemi, Kelvin J Msechu, Arash Ghasemi, Mbakisya A. Onyango, Ignatius Fomunung, Joseph Owino
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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.
Subjects: Machine Learning (cs.LG); Numerical Analysis (math.NA)
Cite as: arXiv:2102.07890 [cs.LG]
  (or arXiv:2102.07890v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2102.07890

Submission history

From: Arash Ghasemi [view email]
[v1] Mon, 15 Feb 2021 23:05:18 UTC (747 KB)
[v2] Fri, 19 Mar 2021 22:23:14 UTC (747 KB)
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