• Login
    View Item 
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Multi-objective fuzzy regression applied to the calibration of conceptual rainfall-runoff models

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    azu_td_9814366_sip1_m.pdf
    Size:
    2.382Mb
    Format:
    PDF
    Download
    Author
    Ozelkan, Ertunga Cem, 1970-
    Issue Date
    1997
    Keywords
    Hydrology.
    Engineering, System Science.
    Advisor
    Duckstein, Lucien
    
    Metadata
    Show full item record
    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
    The purpose of this research is (1) to develop a multi-objective fuzzy regression (MOFR) tool to overcome the shortcomings of the existing fuzzy regression approaches while keeping the good characteristics, and (2) to study systems with uncertain elements, using the example of rainfall-runoff process to illustrate the approach. Previous research has shown that fuzzy regression performs superior compared to statistical regression in some cases. On the other hand, fuzzy regression has also been criticized because it does not allow all data points to influence the estimated parameters, it is sensitive to data outliers, and the prediction intervals become wider as more data are collected. Here, several MOFR techniques are developed to overcome these problems by enabling the decision maker select a non-dominated solution based on the tradeoff between data outliers and prediction vagueness. It is shown that MOFR provides superior results to existing fuzzy regression techniques, and the existing fuzzy regression approaches and classical least squares regression are specific cases of the MOFR framework. The methodology is illustrated with examples from rainfall-runoff modeling, more specifically, conceptual rainfall-runoff (CRR) models are analyzed here. One of the main problems in CRR modeling is dealing with the uncertainty associated with the model parameters which is related to data and/or model structure. A fuzzy CRR (FCRR) framework is proposed herein where every element of the CRR is assumed to be uncertain, taken here as fuzzy. Parameter calibration of FCRR models using newly developed fuzzy regression techniques is also investigated. Applications are provided for a linear CRR model, the experimental two-parameter (TWOPAR) and the six-parameter (SIXPAR) models. The major findings can be summarized as follows: (1) FCRR enables the decision maker to gain insight about the CRR model sensitivity to uncertainty of the model elements, (2) using MOFR for the calibration of FCRR leads to non-convex, constrained, non-linear optimization problems, (3) fuzzy least squares regression model yields to more stable parameter estimates than the non-fuzzy regression model, (4) the methodology is applicable to any dynamic system with discrete modes.
    Type
    text
    Dissertation-Reproduction (electronic)
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Systems and Industrial Engineering
    Degree Grantor
    University of Arizona
    Collections
    Dissertations

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.