Machine-Learning Landslide Susceptibility and Runout Modeling in the Nolichucky River Gorge After Hurricane Helene

Extreme rainfall from Hurricane Helene (September 2024) triggered widespread landslides across the southern Appalachian region, highlighting the need for rapid landslide susceptibility assessments that capture both landslide initiation and downstream runout. Traditional susceptibility models often focus solely on initiation zones, limiting their ability to identify which slopes will generate destructive landslides or where material will travel. This study addresses that gap by (1) integrating Geographic Information System (GIS)-based machine learning susceptibility modeling using ArcGIS Pro: Maximum Entropy (MaxEnt) and Random Forest-Based and Boosted Classification and Regression (FBBC) and (2) the U.S. Geological Survey (USGS) Grfin (Growth, Flow, and Inundation) runout toolbox. The study focuses on the Nolichucky River Gorge in eastern Tennessee and western North Carolina, where intense rainfall (4-20 in;10.1-50.8 cm) triggered numerous shallow landslides. Results provide a framework for emergency response along TN-107 and US-19W corridors, infrastructure vulnerability assessments, and hazard planning in Unicoi and Carter counties.