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Influence of the Variogram Model on an Interpolative Survey Using Kriging Technique | OMICS International| Abstract
ISSN: 2157-7617

Journal of Earth Science & Climatic Change
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  • Research Article   
  • J Earth Sci Clim Change 2015, Vol 6(10): 316
  • DOI: 10.4172/2157-7617.1000316

Influence of the Variogram Model on an Interpolative Survey Using Kriging Technique

Arétouyap Z1*, Njandjock Nouck P1, Nouayou R1, Méli’i JL1, Kemgang Ghomsi FE1, Piepi Toko AD1 and Asfahani J2
1Postgraduate School of Science, Technology and Geosciences, University of Yaounde I, P.O. Box 812 Yaounde, Cameroon
2Applied Geophysics Division, Head Atomic Energy Commission, , P.O. Box 6091 Damascus, Syria
*Corresponding Author : Arétouyap Z, Postgraduate School of Science, Technology and Geosciences, University of Yaounde I, P.O. Box 812 Yaounde, Cameroon, Tel: +237 675086759, Email: [email protected]

Received Date: Aug 06, 2015 / Accepted Date: Oct 21, 2015 / Published Date: Oct 30, 2015


Geostatistics is an efficient and effective method to continuously assess the content, the spatio-temporal distribution and the correlation of a discretely sampled deposit. It begins with an exploratory analysis that evaluates the consistency and distribution of data through histograms and QQ plots, and then a structural analysis that evaluates data correlation and dependency through variogram and finally a predictive analysis using kriging. This predicting method is used in various geographical investigations: meteorology, demography, hydrology, orography, economy, and pollution, etc. Even when using related software, it is generally of the duty of the user to manually select the suitable variogram model. The main objectives of this paper were to highlight how the choice of a variogram model can affect the results of an interpolating predictive analysis and to show how a best-fitted model can be selected. The results, illustrated with an example, show that the choice of the variogram model inevitably influences the results of a kriging at both endpoints and amplitude of the range of the estimated values. However, the direction of variation of the interpolated values is independent of the variogram model: different variogram models (with the same characteristics) produce different thematic maps but, the areas of minimum and maximum values remain unchanged. Fortunately, the computation of some cross validation tests such as mean error (ME), mean square error (MSE), root mean square error (RMSE), average standard error (ASE) and root mean square standardized error (RMSSE) can help to ascertain the performance of the developed models.

Keywords: Kriging; Predictive analysis; Spatial analysis; Structural analysis; Variogram

Citation: Arétouyap Z, Nouck NP, Nouayou R, Méli’i JL, Kemgang Ghomsi FE, et al. (2015) Influence of the Variogram Model on an Interpolative Survey Using Kriging Technique. J Earth Sci Clim Change. 6: 316. Doi: 10.4172/2157-7617.1000316

Copyright: © 2015 Arétouyap Z, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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