Interpolating scattered data points is a problem of wide ranging interest. One of the most popular interpolation methods in geostatistics is ordinary kriging. The price for its statistical optimality is that the estimator is computationally very expensive. We demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined ...

In this paper, we implement and compare the accuracy of ordinary kriging, lognormal ordinary kriging, inverse distance weighting (IDW) and splines for interpolating seasonally stable soil properties (pH, electric conductivity and organic matter) that have been demonstrated to affect yield production. The choice of the exponent value for IDW and splines as well as the number of the closest neigh...

" Kriging " (after the South African mining engineer Danie Krige) is a term used for a family of methods for minimum error variance estimation. Consider a linear estimatê z 0 = ˆ z(r 0) at a location r 0 based on N measurements z = [z(r 1),. .. , z(r N)] T = [z 1 ,. .. , z N ] T ˆ z 0 = w 0 + N i=1 w i z i = w 0 + w T z, (1) where w i are the weights applied to z i. We consider z i as particula...

BACKGROUND Ultraviolet B (UV-B) radiation plays a multifaceted role in human health, inducing DNA damage and representing the primary source of vitamin D for most humans; however, current U.S. UV exposure models are limited in spatial, temporal, and/or spectral resolution. Area-to-point (ATP) residual kriging is a geostatistical method that can be used to create a spatiotemporal exposure model ...

Introduction Conclusions References

In engineering design, an optimized solution often turns out to be suboptimal, when implementation errors are encountered. While the theory of robust convex optimization has taken significant strides over the past decade, all approaches fail if the underlying cost function is not explicitly given; it is even worse if the cost function is nonconvex. In this work, we present a robust optimization...

Robust optimization is concerned with finding an optimal solution that insensitive to uncertainties and has been widely used in solving real-world problems. However, most robust methods suffer from high computational costs poor convergence. To alleviate the above problems, improved algorithm proposed. First, reduce cost, second-order Taylor series surrogate model approximate robustness indices....

In this paper, we applied the support vector machine (SVM) to the spatial interpolation of the multi-year average annual precipitation in the Three Gorges Region basin. By combining it with the inverse distance weighting and ordinary kriging method, we constructed the SVM residual inverse distance weighting, as well as the SVM residual kriging precipitation interpolation model and compared them...

Variograms are used to describe the spatial variability of environmental variables. In this study, the parameters that characterize the variogram are obtained from a variogram in a different but comparably polluted area. A procedure is presented for improving the variogram modelling when data become available from the area of interest. Interpolation is carried out by means of a Bayesian form of...

The high computational cost of population based optimization methods, such as multiobjective genetic algorithms (MOGAs), has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise methods that can significantly reduce the number of simulation (objective/constraint functions) calls. We present a new multi-objective design optimizat...