Multiple parasite infections are widespread in the developing world and understanding their geographical distribution is important for spatial targeting of differing intervention packages. We investigated the spatial epidemiology of mono- and co-infection with helminth parasites in East Africa and developed a geostatistical model to predict infection risk. The data used for the analysis were ta...

[1] The influence of aquifer property correlation on multiphase fluid migration and entrapment was explored through the use of correlated and uncorrelated porosity, permeability, and capillary pressure-saturation (Pc-Sat) parameter fields in a crosssectional numerical multiphase flow model. Data collected from core samples in a nonuniform sandy aquifer were used to generate three-dimensional aq...

in this research, the spatial distribution of electrical conductivity and ph of springs, ghanats and the base flow concentration places on the streams of central and south-west and central parts of the hamedan-bahar plain were evaluated. using different geostatistical methods such as kriging, minimum curvature, inverse distance, natural neighbor, local polynomial and radial basis functions, 108...

A key problem in environmental monitoring is the spatial interpolation. The main current approach in spatial interpolation is geostatistical. Geostatistics is neither the only nor the best spatial interpolation method. Actually there is no “best” method, universally valid. Choosing a particular method implies to make assumptions. The understanding of initial assumption, of the methods used, and...

Stratigraphic modeling based on physical and geologic principles has been improved by more sophisticated process models and increased computer power. However, such efforts may reach a limit in their predictive power because of the stochastic, multiscaled nature of the physical processes involved. Building on techniques from the geostatistical literature, a conditional simulation method, dubbed ...

In this report the main focuses are geostatistical Gaussian Markov random field (GMRF) models and parallel exact sampling of GMRFs. There are also brief overviews of parallel computing and Markov chain Monte Carlo (MCMC) methods, and a literature review of parallel MCMC. The geostatistical GMRF models are constructed by discretising the domain region using a lattice. Instead of giving this latt...

Reliable prediction of subsurface flow and contaminant transport depends on the accuracy with which the values and spatial distribution of process-relevant model parameters can be identified. Successful characterization methods for complex soil systems are based on (1) an adequate parameterization of the subsurface, capable of capturing both random and structured aspects of the heterogeneous sy...