Adaptive random search evaluated as a method for calibration of the SMA-NWSFS model

Persistent Link:
http://hdl.handle.net/10150/278394
Title:
Adaptive random search evaluated as a method for calibration of the SMA-NWSFS model
Author:
Armour, Arthur David, 1964-
Issue Date:
1990
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:
Random search methods are becoming more widely used to estimate model parameters. Their ability to globally search a parameter space makes them attractive for solving problems that have multi-local optima, as are non-linear hydrologic models such as Conceptual Rainfall-Runoff (CRR) models. The investigation of this thesis is on the ability of the Adaptive Random Search (ARS) to find the global optimum of the CRR model known as the Soil Moisture Accounting Model of the National Weather Service River Forecast System (SMA-NWSRFS) and compares its performance to that of the Uniform Random Search (URS). Research results indicate that, although the ARS was slightly more efficient than the URS, neither strategy demonstrated an ability to converge to the globally optimum parameter set. Factors which inhibit the convergence include model structure characteristics and an insufficient number of points searched. Ways for random search techniques to identify and address these problems are discussed.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Hydrology.; Mathematics.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College
Degree Grantor:
University of Arizona
Advisor:
Sorooshian, Soroosh

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAdaptive random search evaluated as a method for calibration of the SMA-NWSFS modelen_US
dc.creatorArmour, Arthur David, 1964-en_US
dc.contributor.authorArmour, Arthur David, 1964-en_US
dc.date.issued1990en_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.abstractRandom search methods are becoming more widely used to estimate model parameters. Their ability to globally search a parameter space makes them attractive for solving problems that have multi-local optima, as are non-linear hydrologic models such as Conceptual Rainfall-Runoff (CRR) models. The investigation of this thesis is on the ability of the Adaptive Random Search (ARS) to find the global optimum of the CRR model known as the Soil Moisture Accounting Model of the National Weather Service River Forecast System (SMA-NWSRFS) and compares its performance to that of the Uniform Random Search (URS). Research results indicate that, although the ARS was slightly more efficient than the URS, neither strategy demonstrated an ability to converge to the globally optimum parameter set. Factors which inhibit the convergence include model structure characteristics and an insufficient number of points searched. Ways for random search techniques to identify and address these problems are discussed.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectHydrology.en_US
dc.subjectMathematics.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSorooshian, Sorooshen_US
dc.identifier.proquest1342640en_US
dc.identifier.bibrecord.b26592125en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.