Theses and Dissertations

Fall 2010

Hong, Sahyun

, nonlinear relations and different qualities. Previous approaches rely on a strong Gaussian assumption or the combination of the source-specific probabilities that are individually calibrated from each data source. This dissertation develops different approaches to integrate diverse earth science data

. First approach is based on combining probability. Each of diverse data is calibrated to generate individual conditional probabilities, and they are combined by a combination model. Some existing models are reviewed and a combination model is proposed with a new weighting scheme. Weakness of the

probability combination schemes (PCS) is addressed. Alternative to the PCS, this dissertation develops a multivariate analysis technique. The method models the multivariate distributions without a parametric distribution assumption and without ad-hoc probability combination procedures. The method accounts

Spring 2011

Wang, Chonghai

mechanisms investigated for what are called uncertain databases or probabilistic databases, where a tuple is associated with a membership probability indicating the level of confidence on the stored information. In this thesis, we study top-k ranking with uncertain data in two general areas. The first is on

show experimentally that pruning can generate orders of magnitude performance gains. In the second area of our investigation, we study the problem of top-k ranking for objects with multiple attributes whose values are modeled by probability distributions and constraints. We formulate a theory of top-k

The goal of top-k ranking is to rank individuals so that the best k of them can be determined. Depending on the application domain, an individual can be a person, a product, an event, or just a collection of data or information for which an ordering makes sense. In the context of databases, top-k

Spring 2010

Alshehri, Naeem S.

technical analysis considering all available data. Current methods of estimating resource uncertainty use spreadsheets or Monte Carlo simulation software with specified probability distributions for each variable. 3-D models may be constructed, but they rarely consider uncertainty in all variables. This

. The CFD approach produced more realistic uncertainty in distributions of the HIIP than those obtained from the BS or SBS approaches. 0-D modeling was used for estimating uncertainty in HIIP with different source of thickness. 2-D is based on geological mapping and can be presented in 2-D maps and

A reliable estimate of the amount of oil or gas in a reservoir is required for development decisions. Uncertainty in reserve estimates affects resource/reserve classification, investment decisions, and development decisions. There is a need to make the best decisions with an appropriate level of

Spring 2024

Valdivia Paucar, Myzael J.

parameter of a material as a random variable with a specific probability distribution. The outcomes of such analyses are evaluated through design acceptance criteria (DAC) that incorporate uncertainty in terms of design reliability – also known as Reliability-Based Design Acceptance Criteria (RBDAC

on a modified open pit slope, inspired by the geotechnical, geological, and hydrological characteristics of an implemented pit slope located in British Columbia, Canada. The outcomes of this research yield significant insights for forthcoming probabilistic open pit slope stability analyses. The

to higher coefficients of variation and thus an elevated probability of failure. These results can be the defining factor between designs deemed acceptable or not, as per the RBDAC criteria. Based on these outcomes, it is suggested that practitioners consider, depending on the necessity of the design

Spring 2016

Stralberg, Diana

Often referred to as North America’s bird nursery, the boreal forest biome provides a productive environment for breeding birds, supporting high species diversity and bird numbers. These birds are likely to shift their distributions northward in response to rapid climate change over the next

) model uncertainty in current and future projections, (2) time lags in ecosystem responses to climate change, (3) the static nature of correlative models, and (4) the influence of historical biogeography in determining current distributions. In my first chapter, using a continental-scale avian dataset

younger forest. In my third chapter, I developed a hybrid modelling approach based on topo-edaphically constrained projections of climate-driven vegetation change potential, coupled with weather- and fuel-based simulations of future wildfires, and projections of large-scale industrial development