Browsing by Subject "Uncertainty Analysis"
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Item Development of an ArcGIS interface and design of a geodatabase for the soil and water assessment tool(Texas A&M University, 2004-09-30) Valenzuela Zapata, Milver AlfredoThis project presents the development and design of a comprehensive interface coupled with a geodatabase (ArcGISwat 2003), for the Soil and Water Assessment Tool (SWAT). SWAT is a hydrologically distributed, lumped parameter model that runs on a continuous time step. The quantity and extensive detail of the spatial and hydrologic data, involved in the input and output, both make SWAT highly complex. A new interface, that will manage the input/output (I/O) process, is being developed using the Geodatabase object model and concepts from hydrological data models such as ArcHydro. It also incorporates uncertainty analysis on the process of modeling. This interface aims to further direct communication and integration with other hydrologic models, consequently increasing efficiency and diminishing modeling time. A case study is presented in order to demonstrate a common watershed-modeling task, which utilizes SWAT and ArcGIS-SWAT2003.Item Numerical Methods for Uncertainty Analysis in Dynamical Systems(2013-12-06) Kim, KyungeunThe current methods for uncertainty analysis in dynamical systems are restricted in terms of computational cost and evaluation domain since they either use grid points or work only along trajectories. To break through these problems we present a new method: the Rothe & maximum-entropy method which follows the steps below. A deterministic dynamical system with initial value uncertainties can be analyzed via the uncertainty propagation which is based on the Liouville equation in the form of the first-order linear partial differential equation. On this equation we conduct a semi-discretization in time via A-stable rational approximations of consistency order k and this yields the stationary spatial problem. This spatial problem now can be solved by the spatial discretization scheme: we propose the maximum-entropy approximation which provides unbiased interpolations even with fewer numbers of scattered points. Through these steps we finally obtain a system of linear equations for the evolution of the probability density function ut, which can be easily solved in several ways. This method can provide more efficiency in terms of computation time thanks to using fewer numbers of scattered points instead of grid points. Also, it enables the constant tracking of probability density functions in a specific fixed domain of interest and this is especially effective for switched systems.