The packages geoR and geoRglm are contributed packages to the statistical software system R, implementing methods for model-based geostatistical data-analysis. In this paper we focus on the capabilities of the packages, the computational implementation and related issues, and indicate directions for future developments. geoR implements methods for Gaussian and transformed Gaussian models. The p...

Direct Neural Network Residual Kriging (DNNRK) is a two step algorithm (Kanevsky et al. 1995). The first step includes estimating large scale structures by using artificial neural networks (ANN) with simple sum of squares error function. ANN, being universal approximators, model overall non-linear spatial pattern fairly well. ANN are model free estimators and depend only on their architecture a...

This paper discusses advantages and shortcomings of the S environment for multivariable geostatistics, in particular when extended with the gstat package, an extension package for the S environments (R, S-Plus). The gstat S package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. In particular, it makes the calculation, simu...

We present a general comparison of punctual kriging based image restoration for different neighbourhood sizes. The formulation of the technique under consideration is based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Three different neighbourhood windows are considered to estimate the semivariance at different lags for studying its effect in reduction of nega...

This paper deals with a theoretical approach to assessing the effects of parameter estimation uncertainty both on Kriging estimates and on their estimated error variance. Although a comprehensive treatment of parameter estimation uncertainty is covered by full Bayesian Kriging at the cost of extensive numerical integration, the proposed approach has a wide field of application, given its relati...

The groundwater quality research is one of the important and its pollution control was included insome research literatures. Ground water quality has spatial and temporal variation so classical statisticscould not account these variations at the regional scale researches. This study usedgeo-statisticalmethodsto optimize an interpolation method in order to estimate the spatial distribution of pH...

A crop height model (CHM) can be an important element of the decision making process in agriculture, because it relates well with many agronomic parameters, e.g., crop height, plant biomass or crop yield. Today, CHMs can be inexpensively obtained from overlapping imagery captured from unmanned aerial vehicle (UAV) platforms or from proximal sensors attached to ground-based vehicles used for reg...

Kriging is one of the most often used prediction methods in spatial data analysis. This paper examines which steps of the underlying algorithms can be performed in parallel on a PVM cluster. It will be shown, that some properties of the so called kriging equations can be used to improve the parallelized version of the algorithm. The implementation is based on R and PVM. An example will show the...

The methods of simple and disjunctive kriging are applied and compared in the estimation of optimum locations of recording raingauges as part of a network for the determination of storm characteristics to be used in forecasting and design. Some advantages are shown but problems arise when there are large differences in storm structures and movements. Another source of uncertainty is in the mode...

In numerical dosimetry, the recent advances in high performance computing led to a strong reduction of the required computational time to assess the specific absorption rate (SAR) characterizing the human exposure to electromagnetic waves. However, this procedure remains time-consuming and a single simulation can request several hours. As a consequence, the influence of uncertain input paramete...