VISUALIZING AND AUTOMATING PAST, REAL-TIME, AND FORECAST DYNAMIC HAZARD MAPS FOR SHALLOW COLLUVIAL LANDSLIDES IN EASTERN KENTUCKY

Landslide hazards are a persistent threat to communities and infrastructure in Eastern Kentucky, where steep slopes, shallow colluvial soils, and variable hydrological conditions make slope failures frequent. This thesis presents an integrated approach to landslide hazard mapping (LHM) through the development of dynamic, spatiotemporal LHMs for shallow colluvial landslides. Two studies within this work investigate and refine the use of the Lu and Godt (2008) factor of safety (FS) equation to improve landslide predictions. The first study establishes a novel LHM workflow using Hydrus-1D to simulate soil moisture infiltration and fluctuations from precipitation and evapotranspiration (ET) data. This study also investigates the application of FS parameters to statistics-based landslide susceptibility maps (LSM). This approach demonstrates the feasibility of generating accurate, predictive landslide maps using publicly accessible data, validated against known landslide sites in Pike and Breathitt Counties. The second study builds upon this workflow, further improving slope stability assessments by refining the depth-to-bedrock (DTB) parameter and introducing soil root cohesion into the FS calculations. By automating data extraction and map production within a Python environment, this work enables efficient, real-time forecast capabilities with 72-hour landslide hazard predictions. This automation facilitates proactive hazard mitigation, with findings highlighting the critical role of vegetation and root cohesion in slope stability. Together, these studies establish a comprehensive, adaptable LHM methodology that leverages both physical modeling and machine learning for accurate, near real-time and real-time landslide forecasting.