Calibration of an Artificial Neural Network for Predicting Development in Montgomery County, Virginia: 1992-2001

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dc.contributor.authorThekkudan, Travis Francisen
dc.contributor.committeechairCampbell, James B. Jr.en
dc.contributor.committeememberCarstensen, Laurence W.en
dc.contributor.committeememberSforza, Peter M.en
dc.contributor.committeememberBoyer, John D.en
dc.contributor.departmentGeographyen
dc.date.accessioned2014-03-14T20:40:29Zen
dc.date.adate2008-07-18en
dc.date.available2014-03-14T20:40:29Zen
dc.date.issued2008-06-11en
dc.date.rdate2008-07-18en
dc.date.sdate2008-06-24en
dc.description.abstractThis study evaluates the effectiveness of an artificial neural network (ANN) to predict locations of urban change at a countywide level by testing various calibrations of the Land Transformation Model (LTM). It utilizes the Stuttgart Neural Network Simulator (SNNS), a common medium through which ANNs run a back-propagation algorithm, to execute neural net training. This research explores the dynamics of socioeconomic and biophysical variables (derived from the 1990 Comprehensive Plan) and how they affect model calibration for Montgomery County, Virginia. Using NLCD Retrofit Land Use data for 1992 and 2001 as base layers for urban change, we assess the sensitivity of the model with policy-influenced variables from data layers representing road accessibility, proximity to urban lands, distance from urban expansion areas, slopes, and soils. Aerial imagery from 1991 and 2002 was used to visually assess changes at site-specific locations. Results show a percent correct metric (PCM) of 32.843% and a Kappa value of 0.319. A relative operating characteristic (ROC) value of 0.660 showed that the model predicted locations of change better than chance (0.50). It performs consistently when compared to PCMs from a logistic regression model, 31.752%, and LTMs run in the absence of each driving variable ranging 27.971% – 33.494%. These figures are similar to results from other land use and land cover change (LUCC) studies sharing comparable landscape characteristics. Prediction maps resulting from LTM forecasts driven by the six variables tested provide a satisfactory means for forecasting change inside of dense urban areas and urban fringes for countywide urban planning.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-06242008-173032en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-06242008-173032/en
dc.identifier.urihttp://hdl.handle.net/10919/33732en
dc.publisherVirginia Techen
dc.relation.haspartThekkudan_Thesis_ETD.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectUrbanen
dc.subjectMontgomery Countyen
dc.subjectModelingen
dc.subjectLand Useen
dc.subjectArtificial Neural Networken
dc.titleCalibration of an Artificial Neural Network for Predicting Development in Montgomery County, Virginia: 1992-2001en
dc.typeThesisen
thesis.degree.disciplineGeographyen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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