Using geographical information systems and neural networks to predict fuel moisture in homogeneous fuels

Persistent Link:
http://hdl.handle.net/10150/278457
Title:
Using geographical information systems and neural networks to predict fuel moisture in homogeneous fuels
Author:
Ball, Barbara Jean, 1955-
Issue Date:
1994
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:
Computer models used to predict the pattern and rate of spread of fire in grasslands as well as other vegetation types rely on various inputs for their calculations. Because of the direct effect they have on the quantity of fuel available to carry a fire and the effects of moisture on the potential for fuel available to carry a fire and the effects of moisture on the potential for fuel to begin burning and to sustain a fire, fuel loading measurements, which are similar to production measurements in grasslands, and estimates of fuel moisture are two important variables to be considered when modeling fire behavior. The objective of this project is to determine if there is a relationship between measured environmental variables and the fuel moisture values at the same sample points which can be modeled with GIS data and neural networks. This study was carried out using a combination of field sampled data and common GIS data layers. The results demonstrate the potential for neural network analysis in this type of environmental problem.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Biology, Ecology.; Agriculture, Forestry and Wildlife.; Artificial Intelligence.; Computer Science.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Renewable natural resources
Degree Grantor:
University of Arizona
Advisor:
Guertin, Phillip

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleUsing geographical information systems and neural networks to predict fuel moisture in homogeneous fuelsen_US
dc.creatorBall, Barbara Jean, 1955-en_US
dc.contributor.authorBall, Barbara Jean, 1955-en_US
dc.date.issued1994en_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.abstractComputer models used to predict the pattern and rate of spread of fire in grasslands as well as other vegetation types rely on various inputs for their calculations. Because of the direct effect they have on the quantity of fuel available to carry a fire and the effects of moisture on the potential for fuel available to carry a fire and the effects of moisture on the potential for fuel to begin burning and to sustain a fire, fuel loading measurements, which are similar to production measurements in grasslands, and estimates of fuel moisture are two important variables to be considered when modeling fire behavior. The objective of this project is to determine if there is a relationship between measured environmental variables and the fuel moisture values at the same sample points which can be modeled with GIS data and neural networks. This study was carried out using a combination of field sampled data and common GIS data layers. The results demonstrate the potential for neural network analysis in this type of environmental problem.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectBiology, Ecology.en_US
dc.subjectAgriculture, Forestry and Wildlife.en_US
dc.subjectArtificial Intelligence.en_US
dc.subjectComputer Science.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.advisorGuertin, Phillipen_US
dc.identifier.proquest1361560en_US
dc.identifier.bibrecord.b32839455en_US
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