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...

iron is an essential nutrients in rice. accurate assessment of this element in paddy soils is very important. improving fertility management, accurate interpolation, being aware of spatial variability and patterns of distribution of micronutrients are the most important factors in soil management and accurate consumption of fertilizers. this study was done for investigating of available iron, d...

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 ...

In this paper, Kriging has been chosen as the method for surrogate construction. The basic idea behind Kriging is to use a weighted linear combination of known function values to predict a function value at a place where it is not known. Kriging attempts to determine the best combination of weights in order to minimize the error in the estimated function value. Because the actual function value...

BACKGROUND A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data. OBJECTIVE We eval...

Sensitivity analysis of capital investments can be effectively carried out by employing a metamodel approach and experimental designs. Although polynomial regression metamodels are popular and straightforward, they do not consider spatial relationships among the data. Dual kriging is an estimation technique that allows the incorporation of spatial correlation into the interpolation or estimatio...

In studies of irrigation and drainage projects for drainage, it is necessary to extend the data from the sampling point to the network. Therefore, based on available data from observational wells, estimating the state of hydraulic conductivity (K) in the surrounding area. The estimation process values for locations where there is no information for them based on viewing areas called wells spati...

In this paper, we consider a practical filtering approach for assimilating irregularly spaced, sparsely observed turbulent signals through a hierarchical Bayesian reduced stochastic filtering framework. The proposed hierarchical Bayesian approach consists of two steps, blending a data-driven interpolation scheme and the Mean Stochastic Model (MSM) filter. We examine the potential of using the d...

In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e., flood flows and low-flow indices) in ungauged catchme...