Soil moisture is an important variable in many environmental researches and application areas as it affects the interactions between atmosphere and land surface by controlling the energy and water exchange. The current measurement techniques are insufficient to acquire accurate large-scale root zone soil moisture (RZSM) data at the spatial resolution of interest. Though assorted models have been successfully applied in relatively small areas to estimate RZSM, the large-scale estimation is still facing challenges as it requires the flexibility and practicality of the models for the applications under various conditions. Though physically based soil moisture models are widely used, the errors in model physics affect the flexibility of these models meanwhile their large demand of data and computational resources reduces the practicality. On the contrary, the statistical and data-driven methods have high potential but their applications for large-scale RZSM estimation have not been fully explored. To develop feasible models for large-scale RZSM estimation using the surface observations, artificial neural networks, specifically multilayer perceptrons (MLPs), were applied in this study to estimate RZSM at the depths of 20cm and 50cm, using the data of 557 stations in the United States. Two experiments including four models were developed and the input variables of the models were carefully selected. The sensitivity analysis found that surface soil moisture and the cumulative rainfall, snowfall, air temperature and surface soil temperature were important inputs. If given soil texture data as inputs, the models achieved better performance and were extremely sensitive to them. The results showed that the MLPs were effective and flexible for the estimation of soil moisture at 20cm under various climate types and were insensitive to the potential errors in soil moisture datasets. However, the results of the estimation at 50cm are not as good as that of the 20cm. / Thesis / Master of Science (MSc)

We propose an efficient wideband theory for adjoint variable sensitivity analysis of problems with general anisotropic materials. The method is formulated based on the transmission line numerical modeling technique. The anisotropic material properties of potential interest are the full tensors of permittivity, permeability, electrical conductivity, magnetic resistivity, magnetoelectric coupling, and electromagnetic coupling. The tensors may contain non-diagonal elements. Our method estimates the gradients of the desired response with respect to all designable parameters using at most one extra simulation, regardless of their number. In contrast, in the conventional sensitivity analysis method using central finite differences, the number of the required simulations scales linearly with the number of designable parameters. The theory has been implemented for sensitivity analysis of the two and three-dimensional structures. The available adjoint variable method (AVM) sensitivities enable the optimization-based design of anisotropic and dispersive anisotropic structures. We apply our AVM technique to optimization-based wideband invisibility cloak design of arbitrary-shape objects. Our method optimizes the voxel-by-voxel constitutive parameters of an anisotropic cloak. This results in a large number of optimizable parameters. The associated sensitivities of a wideband cloaking objective function are efficiently estimated using our anisotropic adjoint variable method technique. A gradient-based optimization algorithm utilizes the available sensitivity information to iteratively minimize the visibility objective function and to determine the constitutive parameters of the optimal cloak. / Thesis / Doctor of Philosophy (PhD)

The inherent uncertainty in travel forecasting models -- arising from errors in input data, parameter estimation, or model formulation -- is receiving increasing attention from the scholarly and practicing community. In this research, we investigate the variance in forecasted traffic volumes resulting from varying the mode and destination choice parameters in an advanced trip-based travel demand model. Using Latin hypercube sampling to construct several hundred combinations of parameters across the plausible parameter space, we introduce substantial changes to mode and destination choice logsums and probabilities. However, the aggregate effects of of these changes on forecasted traffic volumes is small, with a variance of approximately 1 percent on high-volume facilities. Thus, parameter uncertainty does not appear to be a significant factor in forecasting traffic volumes using transportation demand models.

Mining of uranium ore and its extraction using the milling process generates solid and liquid waste, commonly termed uranium mine tailings. Uranium mine tailings is radioactive, as it consists of residual uranium, thorium, and radium, which amounts to 85% of the original ore’s radioactivity. Due to the extensively long half-lives of uranium (4.5x109 years), thorium (75,400 years), and radium (1,620 years) and their harmful radioactive, it is imperative to isolate uranium mine tailings from the environment for a longer period. Containment of uranium mine tailings in dam-like structures, called uranium mine tailings dam (UMTD), is the most followed disposal and storage method. Like a conventional water retention dam, UMTDs are also susceptible to failure, mainly due to adverse weather conditions. Once the UMTD fails, a fraction of the radioactive tailings infiltrates and migrate through the vadose zone contaminating the groundwater sources underlying it. Radionuclide behavior and migration in the subsurface are affected by several environmental factors. To minimize the uncertainty and improve current radionuclide fate and transport models, it is vital to study these factors and any interdependence existing between them. This study aims to understand these environmental factors by i) enlisting the factors affecting subsurface radionuclide migration through scoping review of articles and reports, and ii) analyzing the interdependence existing between the factors using the complex network theory (CNT) approach and identifying the dominant factors among them. Factors such as chemical and biological characteristics of soil stratigraphy, groundwater, and radioactive tailings plume, meteorological, and hydrogeological are found to influence radionuclide behavior and transport mechanisms in the vadose zone. CNT approach described soil microorganisms, fraction of organic carbon, infiltration rate of the soil, transmissivity, clay fraction in the soil, particulates in groundwater, and infiltrating rainwater as dominant factors in the NoF based on their centrality measures and sensitivity analysis of the network of factors (NoF). Any uncertainty associated with these factors will affect and propagate through the model. Hence, sufficient resources should be directed in the future to characterize these factors and minimize their uncertainty, which will lead to developing reliable fate and transport models for radionuclides. / Thesis / Master of Applied Science (MASc) / Waste products from uranium mining and milling operations are called uranium mine tailings, which are radioactive. Generally, uranium mine tailings are disposed of and isolated in dam-like structures referred to as uranium mine tailings dams (UMTD). One of the most common causes of UMTD failure is extreme weather conditions. When a UMTD fails, a part of tailings, consisting of radionuclides uranium, thorium, and radium, infiltrate into the subsurface through the vadose zone. Radionuclide behavior and transport in the subsurface is influenced by several environmental factors. The objective of the present study is to understand the factors affecting radionuclide migration by i) conducting a scoping review on radionuclide migration in the subsurface to describe the factors studied in the literature, and ii) understanding and analyzing any relation among the factors and deriving the most dominant factors based on their relation. This study can be used further to develop accurate and reliable radionuclide fate and transport models with minimal uncertainty.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.