Open AccessApplication of a semivariogram based on a deep neural network to Ordinary Kriging interpolation of elevation data
Author(s) -
Yang Li,
Zhong Baorong,
Xiaohong Xu,
Zijun Liang
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0266942
Subject(s) - variogram , kriging , interpolation (computer graphics) , geostatistics , artificial neural network , multivariate interpolation , computer science , algorithm , nearest neighbor interpolation , gaussian , mathematics , spatial variability , artificial intelligence , statistics , machine learning , bilinear interpolation , physics , motion (physics) , quantum mechanics
The Ordinary Kriging method is a common spatial interpolation algorithm in geostatistics. Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for improving the theoretical accuracy of spatial interpolation. A deep neural network is a machine learning algorithm that can, in principle, be applied to any function, including a semivariogram. Accordingly, a novel spatial interpolation method based on a deep neural network and Ordinary Kriging was proposed in this research, and elevation data were used as a case study. Compared with the semivariogram fitted by the traditional exponential model, spherical model, and Gaussian model, the kriging variance in the proposed method is smaller, which means that the interpolation results are closer to the theoretical results of Ordinary Kriging interpolation. At the same time, this research can simplify processes for a variety of semivariogram analyses.