GSA Connects 2021 in Portland, Oregon

Paper No. 207-10
Presentation Time: 10:45 AM


CRAWFORD, Matthew, Kentucky Geological Survey, University of Kentucky, 228 Mining and Mineral Resources Bldg., Lexington, KY 40506-0107, DORTCH, Jason M., Kentucky Geological Survey, University of Kentucky, Lexington, KY 40506-0107, KOCH, Hudson, University of Kentucky, Kentucky Geological Survey, 228 Mining and Minerals Resources Bldg., 504 Rose Street, Lexington, KY 40506, ZHU, Yichuan, Kentucky Geological Survey, University of Kentucky, 504 Rose Street, 204 Mining and Mineral Resources Building, Lexington, KY 40506-0107 and HANEBERG, William, Kentucky Geological Survey, University of Kentucky, 504 Rose Street, Lexington, KY 40506

The Kentucky Geological Survey completed a multijurisdictional landslide hazard-mitigation plan for five counties in eastern Kentucky. The highly dissected landscape is characterized by narrow ridges and variable hillslope morphologies. Bedrock geology is mapped as packages of flat-lying sedimentary rocks that include thin to thick beds of sandstone, shale, siltstone, coal, and underclays. We assessed landslide susceptibility and risk to identify vulnerable areas and determine the potential impact of landslides on the built environment.

Using LiDAR-derived datasets and landslide inventories, we developed landslide-susceptibility maps using a hybrid machine-learning approach based on hillslope morphology variables. Bagged trees, a machine-learning random-forest classifier, was used to evaluate geomorphic variables; 12 were identified as important: standard deviation of plan curvature, standard deviation of elevation, sum of plan curvature, minimum slope, mean plan curvature, range of elevation, sum of roughness, mean curvature, sum of curvature, mean roughness, minimum curvature, and standard deviation of curvature. These variables were further evaluated using logistic regression modeling to determine probability of landslide occurrence and create a landslide-susceptibility map. The logistic-regression model’s performance was evaluated by the area under the receiver operating characteristic curve, which was 0.83, indicating strong performance. The maps are divided into five classes to increase their utility: low (15.0 percent), low‒moderate (39.2 percent), moderate (23.4 percent), moderate‒high (13.9 percent), and high (3.7 percent).

The risk assessment is a static, socioeconomic approach including potential landslide effects on population, roads, railroads, buildings, and land. We combined vulnerability and consequence data with landslide susceptibility data to calculate a risk factor. Resulting risk factors were classified as low (70.7 percent), moderate (11.0 percent), or high (1.5 percent). The maps and data will be incorporated into hazard mitigation plans that implement measures related to improved spatial assessment of landslide hazards, land use and development, transportation, critical facilities, and safety.