To analyze the input/output behavior of simulation models with multiple responses, we may apply either univariate or multivariate Kriging (Gaussian process) metamodels. In multivariate Kriging we face a major problem: the covariance matrix of all responses should remain positive-definite; we therefore use the recently proposed “nonseparable dependence” model. To evaluate the performance of univ...

spatial autocorrelation (sa) is the correlations of the observed data of an area in the form of spatial pattern. the criterion of sa phenomenon occurrence is when the distribution of one observed variable value follows a particular pattern systematically. the sa analysis is most useful and an important tool for investigating the spatial database, this analysis not only itself gives useful infor...

We report an intelligent image restoration approach by combining the geostatistical interpolation technique of punctual kriging and the machine learning approach of adaptive learning. Digital images degraded from Gaussian white noise are restored by first utilizing fuzzy logic for selecting pixels that need to be kriged. The concept of punctual kriging is then used to estimate the intensity of ...

Kriging is a well-known prediction method. It interpolates the value of an unmeasured location from nearby measured locations. In a traditional Kriging interpolation, a client (an entity that is looking for a prediction for a specific location) asks help from a server (an entity that holds enough measurements collected for Kriging interpolations in a region). Predictions are estimated based on ...

This paper introduces the gstat package for the S language (R, S-PLUS). The package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. Gstat (http://www. gstat.org) was started 10 years ago and was released under the GPL in 1996; it is closely linked to several GIS systems. It was not initially written for teaching purposes, b...

This paper describes the application of Kriging metamodeling in multiple-objective simulation optimization. An Arenabased simulation model of an (s, S) inventory system is utilized to demonstrate the capabilities of Kriging metamodeling as a simulation tool. Response surface methodology and Kriging metamodeling are compared to determine the situations in which one approach might be preferred ov...

Whenever simulation requires much computer time, interpolation is needed. There are several interpolation techniques in use (for example, linear regression), but this paper focuses on Kriging. This technique was originally developed in geostatistics by D. G. Krige, and has recently been widely applied in deterministic simulation. This paper, however, focuses on random or stochastic simulation. ...

Precipitation is a function of many topographical land features as well as geographical locations. Spatial correlations between precipitation and topographical and geographical features can be used to improve estimation of precipitation distributions. In this study, kriging with external drift of topographical information was utilized for determining precipitation spatial distributions in Wyomi...

The interpolation of spatial data has been considered in many different forms. The various forms of kriging are among the best known in the earth sciences although techniques such as inverse distance weighting were and are in use for spatially located data. In the numerical analysis literature various forms of splines and more recently radial basis functions have been developed and used. Becaus...

To model liquid water correctly and to reproduce its structural, dynamic and thermodynamic properties warrants models that account accurately for electronic polarisation. We have previously demonstrated that polarisation can be represented by fluctuating multipole moments (derived by quantum chemical topology) predicted by multilayer perceptrons (MLPs) in response to the local structure of the ...