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Sinkholes are common and naturally occurring in certain areas such as Florida and Southern Georgia. The region’s aquifer is often covered by limestone or dolomite carbonate rock, which are made up of minerals that can dissolve in water under the right conditions. Anthropogenic changes are leading to an increased risk of sinkholes in susceptible areas. The formation of these geologic features is hastened by the improper management of ground water, the increase in watershed pollution and runoff, and the mismanagement of underground fresh and wastewater pipes and structures. The goal of this study is to develop an automated geospatial model to determine areas within the study having a potential high risk for sinkholes. Eleven types of geospatial data were collected, processed, and analyzed in ArcGIS Pro Model Builder to calculate sinkhole vulnerability layers in the study area. The eleven data types were geology, soil, land use, aquifer, ground water measurements, road, fault line, elevation precipitation, and evapotranspiration. From this data, ten sinkhole vulnerability layers were produced: 1) subsidence or surface change, 2) average aquifer well depth, 3) ground water vulnerability (DRASTIC), 4) road density, 5) groundwater travel time, 6) aquifer media (Suwannee Limestone) , 7) geology type, 8) slope, 9) land use, and 10) distance from fault lines. Each layer was reclassified and reassigned a value from 1 to 10 according to its sinkhole vulnerability. The weighted layers were analyzed interpretively using ArcGIS Pro’s weighted sum tool producing a Sinkhole Risk Probability Raster. The sampling tool was used for accuracy assessment by comparing the obtained result with historical sinkhole data. This method showed 77% accuracy between known sinkholes and those shown on the sinkholes probability raster. This study is useful to environmental planners/managers and other stakeholders for decision support.
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