Environmental Science & Technology Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/2748

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Comparison of Infiltration Equations and their Field Validation with Rainfall Simulation
(2006-12-13) Turner, Ellen Rebecca; Felton, Gary K; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Infiltration is a complex process with many factors contributing to the rate. Different approximate equations for infiltration differ in the parameters they require and predict different infiltration rate curves. Five equations including those of Kostiakov, Horton, Holtan, Philip and Green-Ampt were compared to determine which one most accurately predicted measured infiltration rates from rainfall simulation events at two different locations. Parameters were developed from measured infiltration data and laboratory analyses of soil samples. The Green-Ampt, Holtan and Philip equations with respective root mean squared errors of 0.15, 0.17, and 0.19 cmh-1, provided the first, second and third best estimates of infiltration rates, for observed infiltration data at the University of Maryland's Research and Education Center in Upper Marlboro, Maryland. An atypical infiltration curve was observed for the Poplar Hill site on the Eastern Shore of Maryland for which infiltration rate was constant and equal to rainfall rate.
Texture-Based Segmentation and Finite Element Mesh Generation for Heterogeneous Biological Image Data
(2005-04-12) Gudla, Prabhakar R; Montas, Hubert J; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The design, analysis, and control of bio-systems remain an engineering challenge. This is mainly due to the material heterogeneity, boundary irregularity, and nonlinear dynamics associated with these systems. The recent developments in imaging techniques and stochastic upscaling methods provides a window of opportunity to more accurately assess these bio-systems than ever before. However, the use of image data directly in upscaled stochastic framework can only be realized by the development of certain intermediate steps. The goal of the research presented in this dissertation is to develop a texture-segmentation method and a unstructured mesh generation for heterogeneous image data. The following two new techniques are described and evaluated in this dissertation: 1. A new texture-based segmentation method, using the stochastic continuum concepts and wavelet multi-resolution analysis, is developed for characterization of heterogeneous materials in image data. The feature descriptors are developed to efficiently capture the micro-scale heterogeneity of macro-scale entities. The materials are then segmented at a representative elementary scale at which the statistics of the feature descriptor stabilize. 2. A new unstructured mesh generation technique for image data is developed using a hierarchical data structure. This representation allows for generating quality guaranteed finite element meshes. The framework for both the methods presented in this dissertation, as such, allows them for extending to higher dimensions. The experimental results using these methods conclude them to be promising tools for unifying data processing concepts within the upscaled stochastic framework across biological systems. These are targeted for inclusion in decision support systems where biological image data, simulation techniques and artificial intelligence will be used conjunctively and uniformly to assess bio-system quality and design effective and appropriate treatments that restore system health.