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Application of GIS-Based Knowledge-Driven and Data-Driven Methods for Debris-Slide Susceptibility Mapping
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Author(s): Raja Das (East Tennessee State University, USA), Arpita Nandi (East Tennessee State University, USA), Andrew Joyner (East Tennessee State University, USA)and Ingrid Luffman (East Tennessee State University, Johnson City, USA)
Copyright: 2021
Volume: 12
Issue: 1
Pages: 17
Source title:
International Journal of Applied Geospatial Research (IJAGR)
Editor(s)-in-Chief: Donald Patrick Albert (Sam Houston State University, USA)and Samuel Adu-Prah (Sam Houston State University, USA)
DOI: 10.4018/IJAGR.2021010101
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Abstract
Debris-slides are fast-moving landslides that occur in the Appalachian region including the Great Smoky Mountains National Park (GRSM). Various knowledge and data-driven approaches using spatial distribution of the past slides and associated factors could be used to estimate the region's debris-slide susceptibility. This study developed two debris-slide susceptibility models for GRSM using knowledge-driven and data-driven methods in GIS. Six debris-slide causing factors (slope curvature, elevation, soil texture, land cover, annual rainfall, and bedrock discontinuity), and 256 known debris-slide locations were used in the analysis. Knowledge-driven weighted overlay and data-driven bivariate frequency ratio analyses were performed. Both models are helpful; however, each come with a set of advantages and disadvantages regarding degree of complexity, time-dependency, and experience of the analyst. The susceptibility maps are useful to the planners, developers, and engineers for maintaining the park's infrastructures and delineating zones for further detailed geo-technical investigation.
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