Image interpolation has been used spaciously by customary interpolation techniques. Recently, Kriging technique has been widely implemented in simulation area and geostatistics for prediction. In this article, Kriging technique was used instead of the classical interpolation methods to predict the unknown points in the digital image array. The efficiency of the proposed technique was proven usi...

The sustainable management of groundwater resources needs quantitative information on its behaviors in space and time. With the availability of distributed models, which can handle large volume of data, the spatial information of groundwater at a grid pattern is not only useful but is necessary to get reliable results. Various methods, both simple and complicated, are available and are in use t...

Kriging or Gaussian Process Regression is applied in many fields as a non-linear regression model as well as a surrogate model in the field of evolutionary computation. However, the computational and space complexity of Kriging, that is cubic and quadratic in the number of data points respectively, becomes a major bottleneck with more and more data available nowadays. In this paper, we propose ...

Epileptic seizures present a serious danger to the lives of their victims, rendering them unconscious, lacking control, and may even result in death only few seconds after onset. This gives rise crucial need for an effective seizure detection method that is fast, accurate, has potential mass market adoption. Kriging methods have good reputation high accuracy spatial prediction, hence, extensive...

Image denoising is a critical issue in the field of digital image processing. This paper proposes a novel Salt & Pepper noise suppression by developing a Kriging Interpolation Filter (KIF) for image denoising. Gray-level images degraded with Salt & Pepper noise have been considered. A sequential search for noise detection was made using kk window size to determine nonnoisy pixels only. The non...

Kriging is a widely employed technique, in particular for computer experiments, in machine learning or in geostatistics. An important challenge for Kriging is the computational burden when the data set is large. We focus on a class of methods aiming at decreasing this computational cost, consisting in aggregating Kriging predictors based on smaller data subsets. We prove that aggregations based...

The results of estimating the areal average climatological rainfall mean in the Guadalhorce river basin in southern Spain are presented in this paper. The classical Thiessen method and three different geostatistical approaches (ordinary kriging, cokriging and kriging with an external drift) have been used as estimators and their results are compared and discussed. The first two methods use only...

Cation exchange capacity (CEC) is one of the most important soil attributes which control some basic properties of soil such as acidity, water and nutrient retaining capacity. However, the measurement of cation exchange capacity in large areas is time consuming and requires high expenses. One way to save time and expenses is to use simple soil covariates and geostatistical methods in mapping CE...

Kriging is a powerful spatial interpolation technique, especially for irregularly spaced data points, and is widely used throughout the earth and environmental sciences. The estimation at an unsampled location is given as the weighted sum of the circumjacent observed points. The weighting factors depend on a model of spatial correlation. Calculation of the weighting factors is done by minimizin...