A method of assessing near-view scenic beauty models: A comparison of neural networks and multiple linear regression

Persistent Link:
http://hdl.handle.net/10150/292054
Title:
A method of assessing near-view scenic beauty models: A comparison of neural networks and multiple linear regression
Author:
Flynn, Myles M., 1966-
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:
With recent advances in artificial intelligence, new methods are being developed that provide faster, and more consistent predictions for data in complex environments. In the field of landscape assessment, where an array of physical variables effect environmental perception, natural resource managers need tools to assist them in isolating the significant predictors critical for the protection and management of these resources. Recent studies that have utilized neural networks to assist in developing predictive models of scenic beauty that have typically utilized linear regression techniques have found limited success. The goal of this research is to compare NN's with linear regression models to determine their efficiency predictive capability for assessing near view scenic beauty in the Cedar City District of the Dixie National forest (DNF). Results of this study strongly conclude that neural networks are consistently better predictors of near view scenic beauty in spruce/fir dominated forests than hierarchical linear regression models.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Landscape Architecture.; Psychology, Social.; Agriculture, Forestry and Wildlife.; Artificial Intelligence.; Recreation.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Renewable Natural Resources
Degree Grantor:
University of Arizona
Advisor:
Gimblett, Randy

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleA method of assessing near-view scenic beauty models: A comparison of neural networks and multiple linear regressionen_US
dc.creatorFlynn, Myles M., 1966-en_US
dc.contributor.authorFlynn, Myles M., 1966-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.abstractWith recent advances in artificial intelligence, new methods are being developed that provide faster, and more consistent predictions for data in complex environments. In the field of landscape assessment, where an array of physical variables effect environmental perception, natural resource managers need tools to assist them in isolating the significant predictors critical for the protection and management of these resources. Recent studies that have utilized neural networks to assist in developing predictive models of scenic beauty that have typically utilized linear regression techniques have found limited success. The goal of this research is to compare NN's with linear regression models to determine their efficiency predictive capability for assessing near view scenic beauty in the Cedar City District of the Dixie National forest (DNF). Results of this study strongly conclude that neural networks are consistently better predictors of near view scenic beauty in spruce/fir dominated forests than hierarchical linear regression models.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectLandscape Architecture.en_US
dc.subjectPsychology, Social.en_US
dc.subjectAgriculture, Forestry and Wildlife.en_US
dc.subjectArtificial Intelligence.en_US
dc.subjectRecreation.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineRenewable Natural Resourcesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGimblett, Randyen_US
dc.identifier.proquest1387718en_US
dc.identifier.bibrecord.b3774527xen_US
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