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dc.contributor.advisorChairperson, Graduate Committee: Edward L. Mooney.en
dc.contributor.authorMashamba, Able.en
dc.contributor.otherLucy Marshall was a co-author of the article, 'A new approach to watershed management practices assessment using the soil and water assessment tool, SWAT' in the journal 'Journal of soil and water conservation' which is contained within this thesis.en
dc.contributor.otherLucy Marshall was a co-author of the article, 'A case study examining factors affecting the performance of response surface modeling during Bayesian optimization and uncertainty analysis of hydrologic models' in the journal 'Journal of environmental modeling and software' which is contained within this thesis.en
dc.contributor.otherLucy Marshall was a co-author of the article, 'Bayesian constrained optimization and uncertainty analysis using radial basis random local fitting' in the journal 'Journal of stochastic environmental research and risk assessment' which is contained within this thesis.en
dc.contributor.otherLucy Marshall was a co-author of the article, 'Bayesian uncertainty analysis of the distributed hydrology soil-vegetation model using radial basis functions' in the journal 'Journal of environmental modeling and software' which is contained within this thesis.en
dc.date.accessioned2013-06-25T18:39:30Z
dc.date.available2013-06-25T18:39:30Z
dc.date.issued2010en
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/1798
dc.description.abstractThis dissertation presents results of research in the development, testing and application of an automated calibration and uncertainty analysis framework for distributed environmental models based on Bayesian Markov chain Monte Carlo (MCMC) sampling and response surface methodology (RSM) surrogate models that use a novel random local fitting algorithm. Typical automated search methods for optimization and uncertainty assessment such as evolutionary and Nelder-Mead Simplex algorithms are inefficient and/or infeasible when applied to distributed environmental models, as exemplified by the watershed management scenario analysis case study presented as part of this dissertation. This is because the larger numbers of non-linearly interacting parameters and the more complex structures of distributed environmental models make automated calibration and uncertainty analysis more computationally demanding compared to traditional basin-averaged models. To improve efficiency and feasibility of automated calibration and uncertainty assessment of distributed models, recent research has been focusing on using the response surface methodology (RSM) to approximate objective functions such as sum of squared residuals and Bayesian inference likelihoods. This dissertation presents (i) results on a novel study of factors that affect the performance of RSM approximation during Bayesian calibration and uncertainty analysis, (ii) a new 'random local fitting' (RLF) algorithm that improves RSM approximation for large sampling domains and (iii) application of a developed automated uncertainty analysis framework that uses MCMC sampling and a spline-based radial basis approximation function enhanced by the RLF algorithm to a fully-distributed hydrologic model case study. Using the MCMC sampling and response surface approximation framework for automated parameter and predictive uncertainty assessment of a distributed environmental model is novel. While extended testing of the developed MCMC uncertainty analysis paradigm is necessary, the results presented show that the new framework is robust and efficient for the case studied and similar distributed environmental models. As distributed environmental models continue to find use in climate change studies, flood forecasting, water resource management and land use studies, results of this study will have increasing importance to automated model assessment. Potential future research from this dissertation is the investigation of how model parameter sensitivities and inter-dependencies affect the performance of response surface approximation.en
dc.language.isoengen
dc.publisherMontana State University - Bozeman, College of Engineeringen
dc.subject.lcshBayesian statistical decision theory.en
dc.subject.lcshHydrologic models.en
dc.titleBayesian optimization and uncertainty analysis of complex environmental models, with applications in watershed managementen
dc.typeDissertation
dc.rights.holderCopyright Able Mashamba 2010en
thesis.catalog.ckey1750961en
thesis.degree.committeemembersMembers, Graduate Committee: Lucy Marshall (co-chair); Bethany L. Letiecq; Joel Cahoon; Donald Boyden
thesis.degree.departmentMechanical & Industrial Engineering.en
thesis.degree.genreDissertationen
thesis.degree.namePhDen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage187en
mus.identifier.categoryEngineering & Computer Science
mus.identifier.categoryChemical & Material Sciences
mus.relation.departmentMechanical & Industrial Engineering.en_US
mus.relation.universityMontana State University - Bozemanen_US


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