The first step in statistical data analysis is to verify three data features: dependency, stationarity, and distribution. If data are independent, it makes little sense to analyze them geostatisticaly. If data are not stationary, they need to be made so, usually by data detrending and data transformation. Geostatistics works best when input data are Gaussian. If not, data have to be made to be ...

Keeping the water table at a favorable level is quite significant for a sustainable management of groundwater plans. Various management measures need to know the spatial and temporal behavior of groundwater. Therefore, the measurement of groundwater levels are generally carried out at spatially random locations in the field; whereas, most of the groundwater models requires these measurement at ...

In this paper, we implement and compare the accuracy of ordinary kriging, lognormal ordinary kriging, inverse distance weighting (IDW) and splines for interpolating seasonally stable soil properties (pH, electric conductivity and organic matter) that have been demonstrated to affect yield production. The choice of the exponent value for IDW and splines as well as the number of the closest neigh...

We propose a new class of trans-Gaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable t...

The aim of this paper is to compare four different methods for binary classification with an underlying Gaussian process with respect to theoretical consistency and practical performance. Two of the inference schemes, namely classical indicator kriging and simplicial indicator kriging, are analytically tractable and fast. However, these methods rely on simplifying assumptions which are inapprop...

In many global optimization problems motivated by engineering applications, the number of function evaluations is severely limited by time or cost. To ensure that each of these evaluations usefully contributes to the localization of good candidates for the role of global minimizer, a stochastic model of the function can be built to conduct a sequential choice of evaluation points. Based on Gaus...

Keeping the water table at a favorable level is quite significant for a sustainable management of groundwater plans. Various management measures need to know the spatial and temporal behavior of groundwater. Therefore, the measurement of groundwater levels are generally carried out at spatially random locations in the field; whereas, most of the groundwater models requires these measurement at ...

in this paper, two methods have been used: multi-layer perceptron artificial neural network (ann-mlp) and universal kriging to estimate of velocity field. neural network is an information processing system which is formed by a large number of simple processing elements, known as artificial nerves. it is formed by a number of nodes and weights connecting the nodes. the input data are multiplied ...

geostatistical approaches have great importance because they include spatial correlation of geographic data. present study evaluated the efficiency of geostatistical techniques and demonstrated their capabilities in studying the soil variables(soil texture (sand percent), ec and so4-2) in the important plant community of nitraria schoberi in meighan desert, arak. a regular grid on the map compr...