Lessening the adverse effect of the semivariogram model selection on an interpolative survey using kriging technique.

Zakari Arétouyap,Robert Nouayou,Axel Dorian Piépi Toko,Philippe Njandjock Nouck,Jamal Asfahani,Franck Eithel Ghomsi Kemgang

doi:10.1186/s40064-016-2142-4

Zakari Arétouyap, Robert Nouayou + Show 4 more

Open Access

https://doi.org/10.1186/s40064-016-2142-4

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Abstract

Objective Many parameters in environmental, scientific and human sciences investigations need to be interpolated. Geostatistics, with its structural analysis step, is widely used for this purpose. This precious step that evaluates data correlation and dependency is performed thanks to semivariogram. However, an incorrect choice of a semivariogram model can skew all the prediction results. The main objectives of this paper are (1) to simply illustrate the influence of the choice of an inappropriate semivariogram model and (2) to show how a best-fitted model can be selected. This may lessen the adverse effect of the semivariogram model selection on an interpolation survey using kriging technique.MethodsThe influence of the semivariogram model selection is highlighted and illustrated by thematic maps drawn using four different models (Gaussian, magnetic, spherical and exponential). Then, a guideline to select the most suitable model, using mean error (ME), mean square error (MSE), root mean square error (RMSE), average standard error (ASE), and root mean square standardized error (RMSSE), is proposed.ResultsThe choice of a semivariogram model seriously influences the results of a kriging survey 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 semivariogram model: different semivariogram models (with the same characteristics) produce different thematic maps but, the areas of minimum and maximum values remain unchanged. Yet, the suitable model can be selected by means of ME, MSE, RMSE, ASE and RMSSE.ConclusionThe present article illustrates how the use of an inappropriate semivariogram model can seriously distort the results of an evaluation, assessment or prediction survey. To avoid such an inconveniency, a methodical approach based on the computation and analysis of ME, RMSE, ASE, RMSSE and MSE is proposed.

Highlights

Geostatistics is used to address various natural and human problems with a spatial dimension
The direction of variation of the interpolated values is independent of the semivariogram model: different semivariogram models produce different thematic maps but, the areas of minimum and maximum values remain unchanged
The suitable model can be selected by means of mean error (ME), mean square error (MSE), root mean square error (RMSE), average standard error (ASE) and root mean square standardized error (RMSSE)

Summary

Introduction

Assessing a variable is very delicate because it is a matter of interpolating that variable where no measurement has been conducted or, establishing a correlation between data of different natures. For this purpose, several softwares have been developed including ArcGIS and Golden Surfer, and are being widely used by thousands of scientists worldwide for various aims. It is so called BLUE (Best Linear Unbiased Estimator) It is by far the most used method to that purpose in all domains of environmental sciences worldwide (Diodato et al 2013, Arétouyap et al 2014a, b; Nshagali et al 2015; Teikeu Assatse et al 2016). The use of this method is growing with the development of new mining platforms across the New Industrialized Countries (Cameroon, Australia, South Africa, Mexico, Ethiopia, Brazil, Turkey, Philippines, etc.)

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