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dc.contributor.authorCahalan, Matthew David
dc.date.accessioned2016-03-29T04:30:28Z
dc.date.available2016-03-29T04:30:28Z
dc.date.issued2015-12
dc.identifier.othercahalan_matthew_d_201512_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/cahalan_matthew_d_201512_ms
dc.identifier.urihttp://hdl.handle.net/10724/34960
dc.description.abstractThis thesis presents an integrated, effective approach to understanding sinkhole formation dynamics and provides a decision-making tool to mitigate risks associated with sinkhole formation. Comprehensive ordinary least squares (OLS) and geographically weighted regression (GWR) geostatistical models are developed for quantifying sinkhole formation mechanisms in the mantled karst terrain of Dougherty County, Georgia (area: 183 km2). Sinkhole density was determined using a GIS-based sinkhole mapping procedure with ten-meter resolution digital elevation models (DEMs) from 1999 and 2011 and a one-meter 2011 LiDAR DEM. Geostatistical models were performed on two sinkhole density datasets: 1) a spatiotemporal dataset representing newly formed and enlarged sinkholes between 1999 and 2011, and 2) LiDAR-derived sinkholes. Geostatistical results show that geologic, hydrologic, and hydrogeologic variables are most influential in sinkhole formation. Geostatistical prediction results were used with deterministic interpolators to assess the ability of statistically-based methods to predict sinkhole density.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectSinkhole mapping
dc.subjectSinkhole formation mechanism
dc.subjectLiDAR
dc.subjectSpatial statistics
dc.subjectGIS
dc.titleSinkhole formation dynamics and geostatistical-based prediction analysis in a mantled karst terrain
dc.typeThesis
dc.description.degreeMS
dc.description.departmentGeology
dc.description.majorGeology
dc.description.advisorAdam Milewski
dc.description.committeeAdam Milewski
dc.description.committeeTodd C. Rasmussen
dc.description.committeeRobert Hawman


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