In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Krigin...

When analysing data from computationally expensive simulation codes or process measurements, surrogate modelling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualisation and optimisation. Kriging is a popular surrogate modelling technique for data based on deterministic computer experiments. There exist several types of Kriging, mostly dif...

Stochastic simulation of facies or geologic units is important before the assignment of continuous rock properties. Sequential indicator simulation (SIS) remains a reasonable approach when there are no clear genetic shapes that could be put into object-based modeling. Constraining SIS to soft secondary data coming from geological interpretation or geophysical measurements is important. There ar...

| We consider the problem of segmenting a digitized 2D or 3D image consisting of two univariate populations. Assume a-priori knowledge allows incomplete assignment of voxels in the image, in the sense that a fraction of the voxels can be identiied as belonging to population 0 , a second fraction to 1 , and the remaining fraction have no a-priori identiication. Based upon estimates of the short ...

We propose here an interpolation method based on a decomposition of the data in largeand small-scale variation. This decomposition was performed using a two-way directional decomposition, similar to the decomposition used by Cressie in his median-polish kriging (1993), though we applied decomposition by means instead of medians. We considered the effects isolated by the decomposition as associa...

This article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas—contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampl...

Assessing the spatial distribution of weeds within a field is key step to success site-specific weed management strategies. Centaurea diluta (knapweed) an emerging that causing major agronomic problem in southern and central Spain because its large size, difficulty controlling it, high competitive ability. The main objectives this study were examine variability C. density two wheat fields by mu...

Annual average daily traffic (AADT) values have long played an important role in transportation design, operations, planning, and policy making. However, AADT values are almost always rough estimates based on the closest short-period traffic counts, factored up using permanent automatic traffic recorder data. This study develops Kriging-based methods for mining network and count data, over time...

This paper presents the application of Exploratory Spatial Data Analysis (ESDA) and Kriging from GIS (ArcGIS8.3) in disease mapping through the analysis of hepatitis B in China. The research shows that geostatistical analysis techniques such as Kriging and ESDA have a good effect in disease mapping. Kriging methods can express properly the spatial correlation. Furthermore, unlike model-based me...