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Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains vii, 41 p. : ill., maps. Includes abstract. Includes bibliographical references.

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Tunnel ventilation is the design practice of placing exhaust fans and air inlets on opposite ends of animal production facilities and moving air through the building via negative pressure. Increasing air velocity within tunnel ventilated broiler production facilities increases sensible cooling and reduces the need for latent cooling (panting), which improves production efficiency. An air velocity measurement system was developed and measurement density analysis for quantifying air velocity distribution was performed in a 12.19 x 121.9 m commercial broiler production facility. Results showed that axial measurement distances of 3.05 m and 40 measurement points per cross-section produced the most descriptive air velocity distribution maps. Air velocity distribution, mean cross-sectional air velocity, and total facility air flow was assessed in three tunnel ventilated commercial broiler production facilities. These facilities differed in size, design, and equipment configurations (test facility 1 was 18.3 × 170.7 m, test facility 2 was 15.24 × 144.8 m, and test facility 3 was 12.19 × 121.9 m). Air velocity distribution varied within all three facilities. Normalized cross-sectional air velocity was plotted against proportion of total house length to compare the cross-sectional air velocity of the three facilities. House width and physical arrangement of the feed hoppers, heating systems, and tunnel fans are impacting air velocity uniformity and mean cross-sectional air velocity in the three broiler houses. Precision Livestock Farming (PLF) seeks to improve production efficiency and animal well-being by model based control of animal production facilities. Four broiler production facilities were assessed for spatial bird body weight (BW) variability. The facilities were 15.24 × 144.8 m solid side-wall tunnel ventilated broiler houses containing birds at 58 and 59 d of age. Significant differences in BW were found between birds residing at center house (3.47 kg average) and at the side-walls (3.38 kg average) (P = 0.025). This variability in BW could be attributed to any number of environmental, nutrient, or behavioral causes. A discussion of input/control parameters for PLF management of broiler production is presented. Quantification of performance variability within these facilities and defining models for control of input parameters is essential to making PLF management feasible.

No / Sulfate causes various health issues for human if on average daily intake of sulfate is more than 500 mg from drinking-water, air, and food. Moreover, the presence of sulfate in rainwater causes acid rains which has harmful effects on animals and plants. Food is the major source of sulfate intake; however, in areas of South-Punjab, Pakistan, the drinking-water containing high levels of sulfate may constitute the principal source of intake. The spatial behavior of sulfate in groundwater is recorded for South-Punjab province, Pakistan. The spatial dependence of the response variable (sulfate) is modeled by using various variograms models that are estimated by maximum likelihood method, restricted maximum likelihood method, ordinary least squares, and weighted least squares. The parameters of estimated variogram models are utilized in ordinary kriging, universal kriging, Bayesian kriging with constant trend, and varying trend and the above methods are used for interpolation of sulfate concentration. The K-fold cross validation is used to measure the performances of variogram models and interpolation methods. Bayesian kriging with a constant trend produces minimum root mean square prediction error than other interpolation methods. Concentration of sulfate in drinking water within the study area is increasing to the Northern part, and health risks are really high due to poor quality of water.

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Radon is a radioactive gas that is the leading cause of lung cancer in non-smokers. While radon is natural and ubiquitous, higher concentrations greatly increase cancer risk. As such, understanding the spatial distribution of radon potential is key to planning and public health efforts. This project tests a method of determining radon potential using in situ measurements of gamma radiation. The in situ measurements were used to create a raster of gamma emissions in the study region using kriging. The resulting model showed that the operational scale of gamma radiation in the study region was 4.5 km. Indoor radon concentrations were then assigned gamma emission rates from the raster and the two were compared. While there was evidence of an association between higher gamma and high radon, the gamma readings were not quantitatively predictive. As such only categorical predictions of radon potential and risk could be made.

Three forms of multivariate analysis, one very classical and the other two relatively new and little-known, are showcased and enhanced: the first is the Singular Value Decomposition (SVD), which is at the heart of many statistical, and now geostatistical, techniques; the second is the method of Variogram Analysis, which is one way of investigating spatial correlation in one or several variables; and the third is the process of interpolation known as cokriging, a method for optimizing the estimation of multivariate data based on the information provided through variogram analysis. The SVD is described in detail, and it is shown that the SVD can be generalized from its familiar matrix (two-dimensional) case to three, and possibly n, dimensions. This generalization we call the "Tensor SVD" (or TSVD), and we demonstrate useful applications in the field of geostatistics (and indicate ways in which it will be useful in other areas). Applications of the SVD to the tools of geostatistics are described: in particular, applications dependent on the TSVD, including variogram modelling in coregionalization. Variogram analysis in general is explored, and we propose broader use of an old tool (which we call the "corhogram ", based on the variogram) which proves useful in helping one choose variables for multivariate interpolation. The reasoning behind kriging and cokriging is discussed, and a better algorithm for solving the cokriging equations is developed, which results in simultaneous kriging estimates for comparison with those obtained from cokriging. Links from kriging systems to kernel systems are made; discovering kerneIs equivalent to kriging systems will be useful in the case where data are plentiful. Finally, some results of the application of geostatistical techniques to a data set concerning nitrate pollution in the West Salt River Valley of Arizona are described.

Weather radar provides a detailed spatial representation of rainfall over a large area and in a real time basis. It has proven to be a valuable tool for hydrologists, agriculturalists and organisations that require accurate and real time information of rainfall and overcomes many of the disadvantages associated with the traditional raingauge estimate. However one of the shortcomings of the rainfall estimates supplied by weather radars is that there are quality problems associated with radar rainfall images that include ground clutter, beam blocking and anomalous propagation to name a few. To obtain the best rainfall estimate possible, techniques for removing ground clutter (non-meteorological echoes that influence radar data quality) on two-dimensional (20) and three-dimensional (3D) radar rainfall image data sets were developed in this study. The chosen method for estimating the "true" values behind the contaminated data was Kriging, which is considered to be the optimal technique for the spatial prediction of Gaussian data. Kriging has various advantages and disadvantages, which need to be taken into consideration in this type of application. For the radar rainfall images to be repaired in real time a computationally fast and efficient method of estimating the missing contaminated data was needed. This is achieved by exploiting the various properties associated with Kriging. In South Africa, radar volume scan data are currently only available on one-kilometre horizontal grids at one-kilometre intervals above the earth's surface. This may not be an accurate representation of the rainfall that actually reaches ground level. To provide an estimate of the "true" rainfall reaching the earth's surface, an algorithm has been developed that extrapolates the radar data down to ground level. The extrapolation is carried out using a combination of 3D Universal and Ordinary Kriging which is preceded by a rainfall classification algorithm developed and calibrated in this study. The techniques proposed for ground clutter infilling and the extrapolation of radar data to ground level have been tested for their effectiveness and efficiency on a wide range of selected rainfall events and indicate that the methodology is practically useful. The South African Weather Service (SAWS) has recently installed the software to "cleanse" the radar data as it is received in real time. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2005.

“En esta memoria se propone el uso de un software computacional como es el programa Excel, para construir un modelo geoestadístico para la complementación y generación de información hidrogeológica en la zona del Maipo medio, específicamente se desea obtener la permeabilidad en puntos definidos donde esta no es conocida. El “Excel” realizado es de fácil uso, ya que basta con introducir las coordenadas del punto donde se requiere información para que este entregue la permeabilidad estimada en ese punto. Para la generación de información hidrogeológica se utilizan datos de 83 pozos ya existentes en archivos de la DGA, los cuales se trabajan mediante un método geoestadístico llamado Kriging o Krigeado, el cual básicamente utiliza las distancias entre el punto que se desea estimar y los puntos en los cuales ya se conoce información para realizar la estimación. Para el Kriging se construye el variograma experimental el cual es la varianza de las diferencias de los valores de la variable regionalizada en las localizaciones separadas una distancia h, de este variograma construido sólo se toma el 39,5% para ajustar el variograma teórico. Se ajustan diferentes variogramas teóricos y se escoge finalmente el con menor varianza del error, el cual resulta ser el llamado modelo esférico, este posee una varianza del error de 0,0068, con un valor para el alcance de 1500 metros y una meseta de 0,27. Conociendo el variograma teórico con que se trabaja, se realiza la estimación con el método de Kriging. Para hacer esto se debe validar el modelo a utilizar, esto se hace mediante el método llamado live one out, consistente en eliminar uno de los puntos donde se tienen datos de la estimación y estimarlo mediante el resto de los datos, este procedimiento se realiza con todos los puntos de permeabilidad conocida, esperando un error bajo en las estimaciones para poder aseverar la correcta estimación del resto de los puntos requeridos. Esta validación arroja los siguientes resultados. Los cuales dan cuenta de una estimación correcta. Finalmente se concluye que para el área y datos propuestos es posible realizar una estimación confiable de la permeabilidad.