A new kriging predictor is proposed. It gives a better performance over the existing predictor when the constant mean assumption in the kriging model is unreasonable. Moreover, the new predictor seems to be robust to the misspecifications in the correlation parameters. The advantages of the new predictor is demonstrated using some examples from the computer experiments literature.

" 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

Soil moisture (SM) plays a key role in many environmental processes and has a high spatial and temporal variability. Collecting sample SM data through field surveys (e.g., for validation of remote sensing-derived products) can be very expensive and time consuming if a study area is large, and producing accurate SM maps from the sample point data is a difficult task as well. In this study, geosp...

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...

High temporal resolution information on burnt area is needed to improve fire behaviour and emissions models. We used theModerate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly and active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuousmaps of the timing of burnt area for 16 large wildland fires. For each fire, parameters for the kriging model were d...

and Applied Analysis 3 2.2. Universal Kriging for Computing Water Depth in Any Position The basic premise of Kriging interpolation is that every unknown point can be estimated by the weighted sum of the known points: Z∗ 0 n ∑ i 1 λi Zi, 2.1 where Z∗ 0 represents the unknown point, Zi refers to each known point, and λ 0 i is the weight given to it. The Kriging algorithm body is involved in the a...