T194 Landslide Hazard Assessments and Risk Reduction: Data Collection and Modelling Challenges (Posters)
Thursday, 29 October 2020
10:00 AM - 12:00 PM
Meeting Rooms
LANDSLIDE RISK ASSESSMENT IN THE MOUNTAIN STATE: RANDOM FOREST MODELLING IN MAJOR LAND RESOURCE AREAS HELPS SURMOUNT GAPS IN THE WEST VIRGINIA LANDSLIDE INVENTORY AND BETTER ALIGN LIDAR-BASED MAPPING WITH LOCAL GEOLOGIC KNOWLEDGE
Paper number: T194-9
Program: Technical Sessions
Time: 11:20 AM – 11:30 AM
Authors
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Aaron Edward MaxwellWest Virginia University, Morgantown, WV
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Maneesh SharmaNational Energy Technology Laboratory, Morgantown, WV
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Kurt DonaldsonDepartment of Geology & Geography, Morgantown, WV
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Shannon Marie MaynardDepartment of Geology & Geography, Morgantown, WV
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Matthew L. BellState College, PA
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Elizabeth HanwellDepartment of Geology & Geography, Morgantown, WV
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Rachel E. YesenchakWest Virginia University, Morgantown, WV
Abstract
Methodological biases and areal coverage gaps have not allowed use of the older data to model landslide susceptibility; however, Maxwell has made slope failure predictions using random forest machine learning derived from the recent LiDAR-based mapping. The landslide prediction models are segregated into individual USDA Major Land Resource Areas, which generally coincide with physiographic divisions. In exception to USDA boundaries, the Allegheny Plateau and Mountains MLRA was divided into northern and southern sections to align with differences in topography and well-documented bedrock facies changes.
Susceptibility modelling has been accomplished for the Northern Appalachian Ridges and Valley (including very small portions of Northern Blue Ridge and Southern Appalachian Ridges and Valley along the Virginia border), the Cumberland Plateau, and the southern section of the Allegheny Plateau and Mountains. Modelling of the Central Allegheny Plateau (the largest and most populous MLRA in West Virginia) and the northern section of the Allegheny Plateau and Mountains are anticipated in the coming year. When susceptibility modelling is coupled to property and infrastructure maps, the resulting Risk Assessment model appears to accurately delineate high risk areas consistent with local landslide knowledge, even in areas where very few landslide incidences occur in the inventory.
Geological Society of America Abstracts with Programs. Vol. 52, No. 6
doi: 10.1130/abs/2020AM-359967
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