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A platform for research: civil engineering, architecture and urbanism
Predicting Sinkhole Susceptibility in Frederick Valley, Maryland Using Geographically Weighted Regression
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.
Predicting Sinkhole Susceptibility in Frederick Valley, Maryland Using Geographically Weighted Regression
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.
Predicting Sinkhole Susceptibility in Frederick Valley, Maryland Using Geographically Weighted Regression
Doctor, Katarina Z. (author) / Doctor, Daniel H. (author) / Kronenfeld, Barry (author) / Wong, David W. S. (author) / Brezinski, David K. (author)
11th Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst ; 2008 ; Tallahassee, Florida, United States
2008-09-18
Conference paper
Electronic Resource
English
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