The Kriging method is an interpolation scheme that can be used for modeling deter-ministic computer analyses as the realization of a stochastic process. The technique has been recognized as an alternative to the traditional Response Surface method in generating approximation models of computationally expensive CFD analyses. This is due to its ability to interpolate sample data and to model a fu...

This article focuses on the estimation and design aspects of a bivariate collocated cokriging experiment. For large class covariance matrices, linear dependency criterion is identified, which allows best unbiased estimator primary variable in setup to reduce univariate kriging estimator. Exact optimal designs for efficient prediction such simple ordinary reduced models with one-dimensional inpu...

For many practical problems in environmental management, information about soil heavy metals, relative to threshold values that may be of practical importance is needed at unsampled sites. The Hangzhou-Jiaxing-Huzhou (HJH) Plain has always been one of the most important rice production areas in Zhejiang province, China, and the soil heavy metal concentration is directly related to the crop qual...

rainfall is considered a highly valuable climatologic resource, particularly in arid regions. as one of the primaryinputs that drive watershed dynamics, rainfall has been shown to be crucial for accurate distributed hydrologicmodeling. precipitation is known only at certain locations; interpolation procedures are needed to predict this variablein other regions. in this study, the ordinary cokri...

The estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. In this paper, we investigate the spatial distribution of three air pollutants in Tehran’s atmosphere: carbon monoxide (CO), nitrogen dioxide (NO2), and atmospheric particulate matters less ...

The estimation of missing observations is an important research field which has practical applications in many science and engineering disciplines. In analyzing the variability of a particular data set which can be spatially related, classical statistical methods make no use of this type of information; whereas geostatistics accomodates the spatial information of the data set in its regression ...

Stochastic simulation of facies or geologic units is important before the assignment of continuous rock properties. Sequential indicator simulation (SIS) remains a reasonable approach when there are no clear genetic shapes that could be put into object-based modeling. Constraining SIS to soft secondary data coming from geological interpretation or geophysical measurements is important. There ar...

This paper presents three multivariate geostatistical algorithms for incorporating a digital elevation model into the spatial prediction of rainfall: simple kriging with varying local means; kriging with an external drift; and colocated cokriging. The techniques are illustrated using annual and monthly rainfall observations measured at 36 climatic stations in a 5000 km region of Portugal. Cross...

Ground-based rainfall observations are the primary sources of precipitation data used in most developing countries. However, those observations are frequently damaged or incomplete, thus missing data is always a problem. This comparison study examines a number of spatial interpolation methods used to estimate missing monthly rainfall data in the northeast region of Thailand. The comparison was ...