Drought monitoring is a fundamental component of drought risk management. It is normally performed usingvarious drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables.In many instances, drought indices are used for monitoring purposes. Geostatistical methods allow the interpolationof spatially referenced data and the prediction of values fo...

This paper is devoted to determining whether the addition of geological information can improve resource estimate mineral resources. The geochemical data used come from 116 drill holes in Nkout East iron deposit southern Cameroon. These are modeled on Surpac and Isatis softwares represent 3D distribution deposit. Statistical analysis then a variographic study performed spatial variability iron....

BACKGROUND Geostatistical techniques are now available to account for spatially varying population sizes and spatial patterns in the mapping of disease rates. At first glance, Poisson kriging represents an attractive alternative to increasingly popular Bayesian spatial models in that: 1) it is easier to implement and less CPU intensive, and 2) it accounts for the size and shape of geographical ...

In der Arbeit wird die Anwendung von Kriging-Algorithmen zur Einschätzung der Werte jährlicher Regenfälle in ausgewählten Punkten eines Bereiches zur Anfertigung genauer Regenfallkarten dieses Bereiches besprochen. Das Verfahren der Kriging-Approximation besteht aus der Lösung algebraischer Gleichungen, aus mathematischer Modellierung und statischer Optimierung. Es werden ein Approximationsalgo...

Geostatistics has been successfully used to analyse and characterize the spatial variability of environmental properties. Besides providing estimated values at unsampled locations, geostatistics measures the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. This work uses universal block kriging to model and map t...

estimating the amount of variation due to spatial dependence at different scales provides a basis for designing effective experiments. accurate knowledge of spatial structures is needed to inform silvicultural guidelines and management decisions for long term sustainability of forests. furthermore, geostatistics is a useful tool to describe and draw map the spatial variability and estimation of...

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...

Sampling and estimation of geographical attributes that vary across space (e.g., area temperature, urban pollution level, provincial cultivated land, regional population mortality and state agricultural production) are common yet important constituents of many real-world applications. Spatial attribute estimation and the associated accuracy depend on the available sampling design and statistica...

MULTI-OBJECTIVE OPTIMIZATION OF BLAST SIMULATION USING SURROGATE MODEL Toshihiro Tsuga, M.S. George Mason University, 2007 Thesis Director: Dr. Rainald Löhner A multi objective optimization approach using a Kriging model coupled with a Multi Objective Genetic Algorithm (MOGA) is applied to a blast damage maximization problem composed of two objectives, namely number of casualties and damage to ...

Kriging is an interpolation technique that is used to estimate a variable at an unmeasured location from observed values at nearer locations. In this study, it is used to analyze the spatial distributions of the health risk of indoor air pollution. The study case is an air-conditioned office building that has 16 floors, located in Taipei, Taiwan. The Kriging method is used in drawing health ris...