Effect of Soil Data Spatial Distribution for Multivariate Spatial Prediction under Uncertainty - Ghanim Mahmood Dhaher (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
This paper is intended to explain the multivariate spatial prediction in geostatistical analysis. The purpose of this paper to develop an empirical methodology for spatial insurance by studying the fundamental causes for differences in between distributed locations. The objective of this work is to access an approach of optimal model of prediction under uncertainty. Cokriging technique used in this paper through empirical estimation of variograms and cross-variograms in all directions of compass. Multivariate technique applied to obtain fitting theoretical models of covariance function, with their properties. The data adopted 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 is secondary variable (co-variable) is chlorine (Cl). The results of this work shown to suggest fit models with best prediction, through the small variations and constraints of weights clear support of accuracy of cokriging prediction. The outcome showed the models of data adopted nearest of origin models of covariance functions. In conclusion, the prediction of cokriging found that the origin data are the nearest the prediction of cross variogram. The computations are carried out by Matlab language