Environmental and Water Resources Decision-Making Under Uncertainty

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dc.contributor.advisor H. Christopher Frey, Committee Member en_US
dc.contributor.advisor E. Downey Brill, Committee Member en_US
dc.contributor.advisor Kenneth Reckhow, Committee Member en_US
dc.contributor.advisor Morton A. Barlaz, Committee Member en_US
dc.contributor.advisor S. Ranji Ranjithan, Committee Chair en_US
dc.contributor.author Harrison, Kenneth Watson en_US
dc.date.accessioned 2010-04-02T19:15:56Z
dc.date.available 2010-04-02T19:15:56Z
dc.date.issued 2002-12-05 en_US
dc.identifier.other etd-06042002-150804 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/5564
dc.description.abstract "Decision-making under uncertainty" is an important area of study in numerous disciplines. The variety of quantitative methods that have been proposed to address environmental and water resources problems reflects the importance of this subject. In a review of the literature, methods were compared and contrasted and promising areas for future research were identified. Conclusions drawn from the review were that 1) large gains may be realized from cross-disciplinary research, 2) significant benefits may be realized from considering uncertainty, 3) advanced algorithms—probabilistic search methods and efficient methods for Bayesian analysis—and increased computing power should greatly extend the applicability of existing methods, and 4) in particular, decision-theoretic methods that have wide application for sequential decision-making. A new decision-theoretic method, Bayesian programming (BP), was developed that takes advantage of the increased computing power and improvements in Bayesian analysis methods. The method has wide applicability, suitable for problems in which there is 1) uncertainty in the modeling, 2) stochastic behavior in the systems that are modeled, 3) the possibility to reduce uncertainty through data collection, and 4) the opportunity for a recourse decision after a period of data collection. The approach combines systematic search methods (mathematical programming) and Bayesian statistical analysis techniques (Markov chain Monte Carlo) in a decision analysis framework. The BP method is tested with application to a hypothetical, but realistic river basin management problem, using real data from the much-studied Athabasca River in Alberta, Canada. The management problem involves balancing the objectives of pulp mill development and water quality protection (dissolved oxygen). Results from application of the BP method were compared with those applying other methodologies. Examination of the results indicated that the BP method is a practical method worthy of additional research. Ultimately, it is hoped, this research will lead to computer-based tools that will improve environmental and water resources decision-making. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject mathematical programming en_US
dc.subject optimization en_US
dc.subject sampling en_US
dc.subject monitoring en_US
dc.subject decision analysis en_US
dc.title Environmental and Water Resources Decision-Making Under Uncertainty en_US
dc.degree.name PhD en_US
dc.degree.level dissertation en_US
dc.degree.discipline Civil Engineering en_US


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