Results 211 to 220 of about 13,817,849 (263)
Integrating random forest-based regression kriging for analyzing spatial variability of rainfall in arid and semi-arid regions. [PDF]
Sci RepManaf M, Ali Z, Scholz M.
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Hybrid Deep Learning-Geostatistical Mapping of Forest Aboveground Biomass in Lishui, China. [PDF]
Plants (Basel)Qian R +6 more
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Who Is Left Behind and Where? Spatial Inequalities in Healthcare Access Among Women in Bangladesh
Noor STA +4 more
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2020
This chapter discusses an alternative approach to performance-driven surrogate modeling, referred to as nested kriging. The technique involves construction of two levels of surrogates. The first-level model maps the objective space into the geometry parameter space of the system in order to establish the surrogate model domain. The domain is defined as
Slawomir Koziel +1 more
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This chapter discusses an alternative approach to performance-driven surrogate modeling, referred to as nested kriging. The technique involves construction of two levels of surrogates. The first-level model maps the objective space into the geometry parameter space of the system in order to establish the surrogate model domain. The domain is defined as
Slawomir Koziel +1 more
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Robust Kriging models in computer experiments
Journal of the Operational Research Society, 2016In the Gaussian Kriging model, errors are assumed to follow a Gaussian process. This is reasonable in many cases, but such an assumption is not appropriate for the situations when outliers are present. Large prediction errors may occur in those cases and more robust estimation is critical.
Taejin Park +4 more
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2009
Geostatistics is a popular class of statistical methods for estimating, or predicting, the value of a continuous spatial process at unobserved locations given the value of the process at a set of known locations. Spatial prediction of this sort is typically performed using the method known as kriging, which provides estimates that are optimized over ...
Calder, Catherine, Cressie, Noel A
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Geostatistics is a popular class of statistical methods for estimating, or predicting, the value of a continuous spatial process at unobserved locations given the value of the process at a set of known locations. Spatial prediction of this sort is typically performed using the method known as kriging, which provides estimates that are optimized over ...
Calder, Catherine, Cressie, Noel A
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New Approach by Kriging Models to Problems in QSAR
Journal of Chemical Information and Computer Sciences, 2004Most models in quantitative structure and activity relationship (QSAR) research, proposed by various techniques such as ordinary least squares regression, principal components regression, partial least squares regression, and multivariate adaptive regression splines, involve a linear parametric part and a random error part.
Kai-Tai Fang, Hong Yin, Yi-Zeng Liang
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Geostatistical models and kriging
IFAC Proceedings Volumes, 2003Abstract Geostatistics is an application of the theory of random functions to spatially distributed data. Geostatistical methods like kriging were initially proposed in mining and petroleum exploration and found their way back to mainstream statistics more than a decade ago.
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51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference<BR> 18th AIAA/ASME/AHS Adaptive Structures Conference<BR> 12th, 2010
Kriging models have proven useful in estimating complex and computationally expensive analyses. They are capable of interpolating a set of observations by quantifying both longer range variations with parametric trends and shorter range variations with spatial correlations.
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Kriging models have proven useful in estimating complex and computationally expensive analyses. They are capable of interpolating a set of observations by quantifying both longer range variations with parametric trends and shorter range variations with spatial correlations.
openaire +1 more source