A spatial interpolation method based on radial basis function networks incorporating a semivariogram model

Based on the combination of the radial basis function network (RBFN) and the semivariogram, a spatial interpolation method, named improved RBFN, is proposed in this paper. To evaluate the interpolation accuracy of the proposed method, reference surfaces with prescribed semivariograms of different sills and scale parameters are generated. The proposed method as well as two existing methods (ordinary kriging and standard RBFN) is then used in the restoration of these reference surfaces. Among three interpolation methods, the proposed method has the highest interpolation accuracy regardless of the arrangement of sample points. The proposed method is performing well especially when the variance of the reference surface is large. An application of the proposed method to the estimation of the spatial distribution of rainfall also shows that the proposed method can estimate more precisely as compare to the other two existing methods. The proposed method is recommended as an alternative to the existing methods, because it has a clear principle and a simple structure. In addition, it provides more flexibility adjusted with stochastic property.