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    Decision making under uncertainty in systems hydrology.

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    Author
    Davis, Donald Ross
    Issue Date
    1971
    Keywords
    Hydrology.
    Hydrology -- Mathematical models.
    Hydrological forecasting.
    Decision making.
    Systems engineering.
    Bridges -- Design and construction.
    Levees.
    Committee Chair
    Duckstein, Lucien
    
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    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Design of engineering projects involve a certain amount of uncertainty. How should design decisions be taken in face of the uncertainty? What is the most efficient way of handling the data? Decision theory can provide useful answers to these questions. The literature review shows that decision theory is a fairly well developed decision method, with almost no application in hydrology. The steps of decision theoretic analysis are given. They are augmented by the concept of expected expected opportunity loss, which is developed as a means of measuring the expected value of additional data before they are received. The method is applied to the design of bridge piers and flood levees for Rillito Creek, Pima County, Arizona. Uncertainty in both the mean and the variance of the logarithms of the peak flows of Rillito Creek is taken into account. Also shown are decision theoretic methods for: 1) handling secondary data, such as obtained from a regression relation, 2) evaluating the effect of the use of nonsufficient statistics, 3) considering alternate models and 4) regionalizing data. It is concluded that decision theory provides a rational structure for making design decisions and for the associated data collection and handling problems.
    Type
    Dissertation-Reproduction (electronic)
    text
    Degree Name
    Ph. D.
    Degree Level
    doctoral
    Degree Program
    Systems Engineering
    Graduate College
    Degree Grantor
    University of Arizona
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