Persistent Link:
http://hdl.handle.net/10150/617591
Title:
BAYESIAN DECISION ANALYSIS OF A STATISTICAL RAINFALL/RUNOFF RELATION
Author:
Gray, Howard Axtell
Affiliation:
Department of Hydrology & Water Resources, The University of Arizona
Publisher:
Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ)
Issue Date:
1972-10
Rights:
Copyright © Arizona Board of Regents
Collection Information:
This title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.
Abstract:
The first purpose of this thesis is to provide a framework for the inclusion of data from a secondary source in Bayesian decision analysis as an aid in decision making under uncertainty. A second purpose is to show that the Bayesian procedures can be implemented on a computer to obtain accurate results at little expense in computing time. The state variables of a bridge design example problem are the unknown parameters of the probability distribution of the primary data. The primary source is the annual peak flow data for the stream being spanned. Information pertinent to the choice of bridge design is contained in rainfall data from gauges on the watershed but the distribution of this secondary data cannot be directly expressed in terms of the state variables. This study shows that a linear regression equation relating the primary and secondary data provides a means of using secondary data for finding the Bayes risk and expected opportunity loss associated with any particular bridge design and single new rainfall observation. The numerical results for the example problem indicate that the information gained from the rainfall data reduces the Bayes risk and expected opportunity loss and allows for a more economical structural design. Furthermore, the careful choice of the numerical methods employed reduces the computation time for these quantities to a level acceptable to any budget.
Keywords:
Bayesian statistical decision theory.; Runoff -- Mathematical models.
Series/Report no.:
Technical Reports on Hydrology and Water Resources, No. 14
Sponsors:
This research was supported in part by Grant No. 14 -31- 001 -3708 on "Use of Models in Validation of Hydrologic and Related Data" and No. A -032 on "Stochastic Space -Time Models of the Rainfall /Runoff Process," both grants from the Office of Water Resources Research, U. S. Department of Interior.

Full metadata record

DC FieldValue Language
dc.contributor.authorGray, Howard Axtellen
dc.date.accessioned2016-07-26T19:56:33Z-
dc.date.available2016-07-26T19:56:33Z-
dc.date.issued1972-10-
dc.identifier.urihttp://hdl.handle.net/10150/617591-
dc.description.abstractThe first purpose of this thesis is to provide a framework for the inclusion of data from a secondary source in Bayesian decision analysis as an aid in decision making under uncertainty. A second purpose is to show that the Bayesian procedures can be implemented on a computer to obtain accurate results at little expense in computing time. The state variables of a bridge design example problem are the unknown parameters of the probability distribution of the primary data. The primary source is the annual peak flow data for the stream being spanned. Information pertinent to the choice of bridge design is contained in rainfall data from gauges on the watershed but the distribution of this secondary data cannot be directly expressed in terms of the state variables. This study shows that a linear regression equation relating the primary and secondary data provides a means of using secondary data for finding the Bayes risk and expected opportunity loss associated with any particular bridge design and single new rainfall observation. The numerical results for the example problem indicate that the information gained from the rainfall data reduces the Bayes risk and expected opportunity loss and allows for a more economical structural design. Furthermore, the careful choice of the numerical methods employed reduces the computation time for these quantities to a level acceptable to any budget.en
dc.description.sponsorshipThis research was supported in part by Grant No. 14 -31- 001 -3708 on "Use of Models in Validation of Hydrologic and Related Data" and No. A -032 on "Stochastic Space -Time Models of the Rainfall /Runoff Process," both grants from the Office of Water Resources Research, U. S. Department of Interior.en
dc.language.isoen_USen
dc.publisherDepartment of Hydrology and Water Resources, University of Arizona (Tucson, AZ)en
dc.relation.ispartofseriesTechnical Reports on Hydrology and Water Resources, No. 14en
dc.rightsCopyright © Arizona Board of Regentsen
dc.sourceProvided by the Department of Hydrology and Water Resources.en
dc.subjectBayesian statistical decision theory.en
dc.subjectRunoff -- Mathematical models.en
dc.titleBAYESIAN DECISION ANALYSIS OF A STATISTICAL RAINFALL/RUNOFF RELATIONen_US
dc.typetexten
dc.typeTechnical Reporten
dc.contributor.departmentDepartment of Hydrology & Water Resources, The University of Arizonaen
dc.description.collectioninformationThis title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.en
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.