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Jun 20, 2012
Predicting Sinkhole Susceptibility in Frederick Valley, Maryland Using Geographically Weighted Regression
Authors: Katarina Z. Doctor [email protected], Daniel H. Doctor [email protected], Barry Kronenfeld [email protected], David W. S. Wong [email protected], and David K. Brezinski [email protected]Author Affiliations
Publication: Sinkholes and the Engineering and Environmental Impacts of Karst
Abstract
A dataset of 556 collapsed sinkholes covering six 1:24,000 scale geologic quadrangles was analyzed in order to map the relative likelihood of sinkhole formation in Frederick Valley, Maryland, USA. Factors that help predict the density of sinkholes included clustering of sinkholes, geologic structure, rock type, and proximity to: quarries, water bodies, streams, roads, faults, axes of synclines or anticlines, and depth to groundwater. Spatial statistical analyses (K-function, Geographically Weighted Regression (GWR) and Inverse Distance Interpolation) were performed to calculate sinkhole density potential within the study area. K-function analysis was performed to find the clusters over different spatial scales. Using these results sinkhole density was determined for each sinkhole location. Relations of external factors to the calculated interdependent sinkhole density were then examined using Geographically Weighted Regression. The result is a map of sinkhole susceptibility that considers geologic, hydrologic, and anthropogenic factors. The results show that the proximity to the groundwater table, proximity to fold axes, proximity to faults, and proximity to quarries are the factors that significantly influence new sinkhole development, in order of decreasing significance. These results may be a guide for future development activities in this region and others like it.
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© 2008 American Society of Civil Engineers.
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Published online: Jun 20, 2012
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Department of Geography, George Mason University, 4400 University Drive MS 1E2, 22030, Fairfax, VA. E-mail: [email protected]
U.S. Geological Survey, 12201 Sunrise Valley Drive, MS 926A, Reston, VA 20192. E-mail: [email protected]
Department of Geography, George Mason University, 4400 University Drive MS 1E2, 22030, Fairfax, VA. E-mail: [email protected]
Earth Systems and GeoInformation Sciences, George Mason University, 4400 University Drive, Room 330 Research I, 22030, Fairfax, VA. E-mail: [email protected]
Maryland Geological Survey, 2300 St. Paul Street, Baltimore, MD 21218. E-mail: [email protected]
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