An Improved Kriging Interpolation Technique Based on SVM and Its Recovery Experiment in Oceanic Missing Data

DOI: 10.4236/ajcm.2012.21007   PDF   HTML   XML   4,753 Downloads   9,186 Views   Citations

Abstract

In Kriging interpolation, the types of variogram model are very finite, which make the variogram very difficult to describe the spatial distributional characteristics of true data. In order to overcome its shortage, an improved interpolation called Support Vector Machine-Kriging interpolation (SVM-Kriging) was proposed in this paper. The SVM-Kriging uses Least Square Support Vector Machine (LS-SVM) to fit the variogram, which needn’t select the basic variogram model and can directly get the optimal variogram of real interpolated field by using SVM to fit the variogram curve automatically. Based on GODAS data, by using the proposed SVM-Kriging and the general Kriging based on other traditional variogram models, the interpolation test was carried out and the interpolated results were analyzed contrastively. The test show that the variogram of SVM-Kriging can avoid the subjectivity of selecting the type of variogram models and the SVM-Kriging is better than the general Kriging based on other variogram model as a whole. Therefore, the SVM-Kriging is a good and adaptive interpolation method.

Share and Cite:

Z. Huang, H. Wang and R. Zhang, "An Improved Kriging Interpolation Technique Based on SVM and Its Recovery Experiment in Oceanic Missing Data," American Journal of Computational Mathematics, Vol. 2 No. 1, 2012, pp. 56-60. doi: 10.4236/ajcm.2012.21007.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] R. D. Zhang, “Spatial Variability Theory and Its Application,” Science Press, Beijing, 2005
[2] V. N. Vapnik, “The Nature of Statistical Learning Theory,” Springer-Verlag, New York, 1995.
[3] J. A. K. Suykens, J. Vannderwalle and B. D. Moor, “Optimal Control by Least Squares Support Vector Machine,” Neural Network, Vol. 14, No.1, 2001, pp. 23-35. doi:10.1016/S0893-6080(00)00077-0
[4] K. F. Liu and R. Zhang, “Minimal Risk Based on the Structure of the Support Vector Machine Methods and Its Application in Numerical Forecast Optimization of Subtropical High,” Journal of Basic Science and Engineering, Vol. 14, No. 3, 2006, pp. 384-389.

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.