The dynamic stall phenomenon is characterized by the formation of a leading-edge vortex, which responsible for adverse aerodynamic forces and moments adversely impacting structural strength life system. Aerodynamic shape optimization (ASO) provides cost-effective approach to delay or mitigate characteristics. Unfortunately, ASO requires multiple evaluations accurate but time-consuming computati...

Spatial cross-correlation among flood sequences impacts the accuracy of regional predictors. Our study investigates this impact for two regionalization procedures, generalized least squares (GLS) regression and top-kriging (TK), which deal with in fundamentally different ways therefore might be associated uncertainty predicted quantiles. We perform a Monte Carlo experiment based on dataset annu...

Travel demand models have been developed and refined over the years to consider a characteristic normally found in travel data: spatial autocorrelation. Another important feature of data is its multivariate nature. However, regarding public transportation demand, there lack that scarce nature data, which generally are expensive collect, also need an appropriate level detail. Thus, main aim this...

We consider the problem of the interpolation of irregularly spaced spatial data, applied to observation of Cosmic Microwave Background (CMB) anisotropies. The well-known interpolation methods and kriging are compared to the binning method which serves as a reference approach. We analyse kriging versus binning results for different resolutions and noise level in the original data. Most of the ti...

and Salas (1985) compared kriging with several other interpolation techniques, including inverse distance, for The choice of an optimal interpolation technique for estimating annual precipitation distributions and found kriging to soil properties at unsampled locations is an important issue in sitebe superior to inverse distance weighting. Warrick et specific management. The objective of this s...

Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial...

The existence of nonstationarity, or spatial variability in geographical relationships, is a topic that has received some attention in the geographical literature in recent years. Its effect in regression-based spatial interpolation methods, however, remains an open research question. In order to explore this question, the paper describes a general regression model which can be used to derive a...

This paper applies a recently developed optimization method to examine the design of networks that monitor radiation under routine conditions. Annual gamma dose rates were modelled by combining regression with interpolation of the regression residuals using spatially exhaustive predictors and an anisotropic variogram of the residuals. Locations of monitoring stations were optimized by minimizin...

Following the Gauss-Markov theorem the generalized lest-squares estimator is the best linear unbiased estimator but following the kriging theory its use is limited. This paper shows the kriging constraint on the classic generalized least-squares estimator.