This survey considers the optimization of simulated systems. The simulation may be either deterministic or random. The survey reflects the author’s extensive experience with simulationoptimization through Kriging (or Gaussian process) metamodels using a frequentist (non-Bayesian) approach. The analysis of Kriging metamodels may use bootstrapping. The survey discusses both parametric bootstrappi...

In this research, a hybrid wavelet-artificial neural network (WANN) and a geostatistical method were proposed for spatiotemporal prediction of the groundwater level (GWL) for one month ahead. For this purpose, monthly observed time series of GWL were collected from September 2005 to April 2014 in 10 piezometers around Mashhad City in the Northeast of Iran. In temporal forecasting, an artificial...

Linear inverse Gaussian problems is traditionally solved using least squares based inversion. The center of the posterior Gaussian probability distribution is often chosen as the solution to such problems, while the solution is in fact the posterior Gaussian probability distribution itself. We present an algorithm, based on direct sequential simulation, which can be used to efficiently draw sam...

Gaussian process modeling (also known as kriging) is an empirical modeling approach that has been widely applied in engineering for the approximation of deterministic functions, due to its flexibility and ability to interpolate observed data. Despite its statistical properties, Gaussian process models (GPM) have not been employed to describe the dynamics of stochastic systems with multiple outp...

This study applies variogram analyses of normalized difference vegetation index (NDVI) images derived from SPOT HRV images obtained before and after the ChiChi earthquake in the Chenyulan watershed, Taiwan, as well as images after four large typhoons, to delineate the spatial patterns, spatial structures and spatial variability of landscapes caused by these large disturbances. The conditional L...

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 ...

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 ...

Abstract. Spatial Gaussian Processes, alias spatial linear models or Kriging estimators, are a powerful and wellestablished tool for the design and analysis of computer experiments in a multitude of engineering applications. A key challenge in constructing spatial Gaussian processes is the training of the predictor by numerically optimizing its associated maximum likelihood function depending o...

This paper addresses the issue of modelling the uncertainty about the value of continuous soil Ž . attributes, at any particular unsampled location local uncertainty as well as jointly over several Ž . locations multiple-point or spatial uncertainty . Two approaches are presented: kriging-based and Ž . simulation-based techniques that can be implemented within a parametric e.g. multi-Gaussian o...

Kriging or Gaussian Process Regression is applied in many fields as a non-linear regression model as well as a surrogate model in the field of evolutionary computation. However, the computational and space complexity of Kriging, that is cubic and quadratic in the number of data points respectively, becomes a major bottleneck with more and more data available nowadays. In this paper, we propose ...