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Groundwater Surface Interpolation, Gaussian Random field, Assumptions, Semivariogram function, Covariance Function, Simple Kriging interpolation method
In this research used Simple Kriging method as one of Geostatistics interpolation methods on the measured value of the specific part in Erbil. Geostatistics is a set of (tools and models) that are developed for statistical analysis of any continuous data that can be observed or measured at any location in the space. Verify three data features in statistical continuous data analysis: dependency, stationery and distribution. With these features you can proceed to the modeling of the Geostatistical data analysis like Kriging. Additionally, the goal of this work is to predict a new value at the unmeasured location by two models and compare the results of these two models based on the Simple Kriging method and understanding their spatial variability. The first step is modeling spatial dependency by semivariogram function. The Two types of the semivariogram are emphasized in this work (Exponential and Gaussian) model, and then different fitting models were taken to describe analyzing their influence over the interpolation results. The source of dataset is the observed values of the (550) wells that had been taken from known specific place called Qushtappa- in Erbil Governorate. Results of applying both models show that the predicted value by anisotropy semivariogram model is better than the isotropy semivarogram model depending on the value of the depth of groundwater. Additionally, the values of (RMSE, SME and SE) of each model are compared and the smaller values of them are the better interpolation as shown in analyzing to evaluate the precision of the prediction.
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