MSc., Faculty of Science, University of the Witwatersrand, 2011 / The study investigates models and processes that could be used by decision-makers to influence policy. The objectives of the study are to illustrate the considerations in the use of information systems and modeling in the context of sustainable development in South Africa. The problem considered is that of measuring South Africa‟s progress toward sustainable development. The study is conducted at a national level. The design uses statistical indicators arranged into a model based information system using the Bellagio principles of 1996. The statistical indicators are assembled from the United Nations 1993 System of National Accounts, UN 1995 Driver-Pressure-State-Impact-Response framework, United Nations 2003 System of integrated Environmental and Economic Accounting, Basel Committee‟s 2004 Basel II framework, and South African Presidency 2010 Developmental indicators frameworks. The South African economy is conceptualised as a dynamic system composed of five types of capital. The fitted model is a vector autoregressive time series model of order p on a set of statistical factors that describe the South African economy. The robustness of the model to assumptions is evaluated using sensitivity and uncertainty analysis. The optimality of the model output for decision making is evaluated using decision theory. The study will facilitate an evidence based approach to managing South Africa‟s progress towards sustainable development.

Thesis (M.S.) University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 20, 2007) Vita. Includes bibliographical references.

Thesis (M.S.)--Ohio University, August, 2004. / Includes bibliographical references (p. 185-187).

Thesis (Ph. D.)--Missouri University of Science and Technology, 2008. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 28, 2009) Includes bibliographical references.

Thesis (M.S.)--Missouri University of Science and Technology, 2008. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 5, 2009) Includes bibliographical references (p. 126-132).

Thesis (M.S.)--Ohio University, August, 2004. / Title from PDF t.p. Includes bibliographical references (p. 185-187)

No description available.

Abstract not available

Thesis (Ph.D.)--McMaster University, 2005. / Supervisor: N.K. Nikolova and M.H. Bakr. Includes bibliographical references (leaves 134-141). Mode of access:World Wide Web.

Global Sensitivity Analysis (GSA) approach helps to identify the effectiveness of model parameters or inputs and thus provides essential information about the model performance. The effects of 14 parameters and one input (forcing data) of the Sacramento Soil Moisture Accounting (SAC-SMA) model are analyzed by using two GSA methods: Sobol' and Fourier Amplitude Sensitivity Test (FAST). The simulations are carried out over five sub-basins within the Columbia River Basin (CRB) for three different periods: one-year, four-year, and seven-year. The main parameter sensitivities (first-order) and the interactions sensitivities (second-order) are evaluated in this study. Our results show that some hydrological processes are highly affected by the simulation length. In other words, some parameters reveal importance during the short period simulation (e.g. one-year) while other parameters are effective in the long period simulations (e.g. four-year and seven-year). Moreover, the reliability of the sensitivity analysis results is compared based on 1) the agreement between the two sensitivity analysis methods (Sobol' and FAST) in terms of highlighting the same parameters or input as the most influential parameters or input and 2) how the methods are cohered in ranking these sensitive parameters under the same conditions (sub-basins and simulation length). The results show that the coherence between the Sobol' and FAST sensitivity analysis methods. Additionally, it is found that FAST method is sufficient to evaluate the main effects of the model parameters and inputs. This study confirms that the Sobol' and FAST methods are reliable GSA methods that can be applied in different scientific applications. Finally, as a future work, we suggest to study the uncertainty associated with the sensitivity analysis approach regarding the reliability of evaluating different sensitivity analysis methods.