High iron concentration and pH change detected using statistics and geostatistics in crystalline basement equatorial region

Nshagali, B. G.; Nouck, Philippe Njandjock; Meli’i, Jorelle Larissa; Arétouyap, Zakari; Manguelle-Dicoum, Eliézer

doi:10.1007/s12665-014-3893-2

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…The parameter to be estimated is measured discretely and then, to obtain the continuous information, kriging technique is used. Nowadays, this technique based on semivariogram is used by so many scientists in various fields as civil protection (Zamani and Mirabadi 2011), meteorology (Caridad and Jury 2013; Arétouyap et al 2014a), geochemistry (Gorai and Kumar 2013; Méli’i et al 2013; Arétouyap et al 2014b; Nshagali et al 2015; Arétouyap et al 2015). If authors do not take into account the paramount impact of the semivariogram model in such investigations, the survey will be sketchy and results untruthful.…”

Section: Discussionmentioning

confidence: 99%

“…It is so called BLUE (Best Linear Unbiased Estimator). Thus, 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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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