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dc.contributor.authorEskandari, Abdollah,1952-
dc.creatorEskandari, Abdollah,1952-en_US
dc.date.accessioned2011-11-28T13:31:47Z
dc.date.available2011-11-28T13:31:47Z
dc.date.issued1997en_US
dc.identifier.urihttp://hdl.handle.net/10150/191213
dc.description.abstractWatershed ecosystems consist of numerous resources which have important environmental, social, cultural, and economic values. The mutual existence and interaction among different resources within the watershed ecosystem calls for a multiobjective watershed resources management analysis. These objectives are often uncertain since they are based on estimation and/or measurement data. Probabilistic methods or fuzzification are usually the methods used in modeling these uncertainties. Selection of the best decision alternative is based on using some Multiple Criterion Decision Making (MCDM) technique. Through simulation in this dissertation, we examine the probabilistic model to address the watershed management problem. In particular, the distance-based methods, which are the most frequently used MCDM techniques, are employed in the problem analysis. In most cases, several interest groups with conflicting preferences are willing to influence the final decision. In our study, a new method is suggested to incorporate their preference orders into the DM's final preference. The application of MCDM techniques combined with stochastic simulation and conflicting preference orders is new in the watershed management literature. Detailed analysis and comparison of the numerical results will help to decide on the suitability of the MCDM technique in watershed resources management. In particular, our numerical results indicate that in practical applications the best alternative selection is significantly influenced by the uncertainties in the payoff values. Hence, in situations where suitable data are available, our methodology is highly recommended.
dc.language.isoenen_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.subjectWatershed management.en_US
dc.subjectHydrology.en_US
dc.titleDecision support system in watershed management under uncertainty.en_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.typetexten_US
dc.contributor.chairSzidarovsky, Ferencen_US
dc.identifier.oclc221689143en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberDuckstein, Lucienen_US
dc.contributor.committeememberLopes, Vicenteen_US
dc.contributor.committeememberFfolliott, Peteren_US
dc.contributor.committeememberFernandez, Emmanuelen_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh. D.en_US
dc.description.notehydrology collectionen_US
refterms.dateFOA2018-08-24T08:38:00Z
html.description.abstractWatershed ecosystems consist of numerous resources which have important environmental, social, cultural, and economic values. The mutual existence and interaction among different resources within the watershed ecosystem calls for a multiobjective watershed resources management analysis. These objectives are often uncertain since they are based on estimation and/or measurement data. Probabilistic methods or fuzzification are usually the methods used in modeling these uncertainties. Selection of the best decision alternative is based on using some Multiple Criterion Decision Making (MCDM) technique. Through simulation in this dissertation, we examine the probabilistic model to address the watershed management problem. In particular, the distance-based methods, which are the most frequently used MCDM techniques, are employed in the problem analysis. In most cases, several interest groups with conflicting preferences are willing to influence the final decision. In our study, a new method is suggested to incorporate their preference orders into the DM's final preference. The application of MCDM techniques combined with stochastic simulation and conflicting preference orders is new in the watershed management literature. Detailed analysis and comparison of the numerical results will help to decide on the suitability of the MCDM technique in watershed resources management. In particular, our numerical results indicate that in practical applications the best alternative selection is significantly influenced by the uncertainties in the payoff values. Hence, in situations where suitable data are available, our methodology is highly recommended.


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