Effect of Soil Data Spatial Distribution for Multivariate Spatial Prediction under Uncertainty (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
This paper is intended to explain the multivariate spatial prediction in geostatisticalanalysis. The purpose of this paper to develop an empirical methodology for spatial insuranceby studying the fundamental causes for differences in between distributed locations. Theobjective of this work is to access an approach of optimal model of prediction underuncertainty. Cokriging technique used in this paper through empirical estimation ofvariograms and cross-variograms in all directions of compass. Multivariate technique appliedto obtain fitting theoretical models of covariance function, with their properties. The dataadopted from Mosul quadrangle/Iraq, include two sets of groundwater viability of soil data.,each set contains (100) sample. First set primary variable, magnesium (Mg), and second set issecondary variable (co-variable) is chlorine (Cl). The results of this work shown to suggest fitmodels with best prediction, through the small variations and constraints of weights clearsupport of accuracy of cokriging prediction. The outcome showed the models of data adoptednearest of origin models of covariance functions. In conclusion, the prediction of cokrigingfound that the origin data are the nearest the prediction of cross variogram. The computationsare carried out by Matlab language