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The current research tackles the performance of Spatiotemporal Interpolation Techniques using the Kriging Technique after relating it to time, which is introduced to the Prediction Process as the reliable mathematical formula to obtain the best performance of a proposed mathematical model. This study's main objective is to evaluate the best Unbiased Linear Prediction Technique with the slightest variance of error through mathematical equations that are derived and related to time.
In this study, the researcher used Spatiotemporal Data of Soil Pollution with minerals in the industrial zone in Mosul city with the actual locations. The data consists of (192) real observations of Arsenic (As) and Chrome (Cr) in the AL Karama Industrial Zone, and this data represents the depth with the actual locations. The Kriging Technique and Kriging Covariance through the mathematical formula are related to time in this research. A function for the place was applied, namely, the variogram function that represents the difference between the observations, as this function was determined for all the directions of the compass, and its parameters were estimated. Through the covariance and the standards of error, it was concluded that the ideas of the Mathematical Spatiotemporal model express the positivity of the proposed model amongst the models of the Covariance functions, such as the Spherical model and the Exponential model, which are approximate models from the principal point of view to the characteristics of the Kriging mode. We also recommend entering three-dimensional data to obtain a proposed mathematical model or data for infectious diseases and atmospheric gas Pollution, using other Spatiotemporal Prediction methods and linking them with artificial intelligence and Fuzzy methods. All the calculations were conducted using the MATLAB Language.
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