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

Persistent Link:
http://hdl.handle.net/10150/282486
Title:
Multi-objective fuzzy regression applied to the calibration of conceptual rainfall-runoff models
Author:
Ozelkan, Ertunga Cem, 1970-
Issue Date:
1997
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)
Keywords:
Hydrology.; Engineering, System Science.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Systems and Industrial Engineering
Degree Grantor:
University of Arizona
Advisor:
Duckstein, Lucien

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleMulti-objective fuzzy regression applied to the calibration of conceptual rainfall-runoff modelsen_US
dc.creatorOzelkan, Ertunga Cem, 1970-en_US
dc.contributor.authorOzelkan, Ertunga Cem, 1970-en_US
dc.date.issued1997en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © 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.en_US
dc.description.abstractThe 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.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectHydrology.en_US
dc.subjectEngineering, System Science.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorDuckstein, Lucienen_US
dc.identifier.proquest9814366en_US
dc.identifier.bibrecord.b37741561en_US
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