a simple but novel and applicable approach is proposed to solve the problem of smoothing effect of ordinary kriging estimate which is widely used in mining and earth sciences. it is based on transformation equation in which z scores are derived from ordinary kriging estimates and then rescaled by the standard deviation of sample data and the sample mean is added to the result. it bears the grea...

[Xu et al. 2009] introduce a graph prediction method that is accurate but slow. My project investigates faster methods based on theirs that are nearly as accurate. If we haveN nodes, their method isO(N), whereas mine are O ( O +OP ) if we have O observations and P predictions to make.

Real-time assessment of the ambient air quality has gained an increased interest in recent years. To give support to this evolution, the statistical air pollution interpolation model RIO is developed. Due to the very low computational cost this interpolation model is an efficient tool for an environment agency when performing real-time air quality assessments. Beside this, a reliable interpolat...

A kriging modeling method is proposed to conduct the temperature uncertainty analysis of an injection mechanism in squeeze casting. A mathematical model of temperature prediction with multi input and single output is employed to estimate the temperature spatiotemporal distributions of the injection mechanism. The kriging model applies different weights to the independent variables according to ...

Atmospheric data is often recorded at scattered station locations. While the data is generally available over a long period of time it cannot be used directly for extracting coherent patterns and mechanistic correlations. The only recourse is to spatially and temporally interpolate the data both to organize the station recording to a regular grid and to query the data for predictions at a parti...

In many pharmaceutical process operations, first-principle models describing the behavior of the processed powders are difficult to derive or computationally very expensive and thus can be considered as “black-box” processes. Kriging is an efficient data-driven methodology that has been shown previously to predict steady-state operation of black-box processes while providing error estimates for...

This paper concentrates on the computational challenge in multidisciplinary design optimization (MDO) and a comprehensive strategy combining enhanced collaborative optimization (ECO) and kriging approximation models is introduced. In this strategy, the computational and organizational advantages of original collaborative optimization (CO) are inherited by ECO, which can satisfy the strengthened...

In this article, we review and compare a number of methods of spatial prediction. To demonstrate the breadth of available choices, we consider both traditional and more-recently-introduced spatial predictors. Specifically, in our exposition we review: traditional stationary kriging, smoothing splines, negative-exponential distance-weighting, Fixed Rank Kriging, modified predictive processes, a ...

Aerodynamic analysis in motorsport is conducted using three methods, computational, scaled experimental and full-scale operational. However, the varying fidelity, different sampling resolutions and unavoidable errors of each technique make valid comparisons between datasets from each method difficult and time consuming. Kriging is a geostatistical method to estimate values within a data field b...

Eight interpolation methods are implemented and tested over a 60 km × 60 km region in Korea, using the results from automatic stereo-matching of SPOT images. First, with the simulated elevation data from digitised contours of paper maps, Kriging and Multiquadric methods show higher performance: with the accuracy better than 3 m than other methods and the speed of ~400 s on a Sparc 20 machine. S...