Application of GIS-Based Knowledge-Driven and Data-Driven Methods for Debris-Slide Susceptibility Mapping

Raja Das, Arpita Nandi, Andrew Joyner and Ingrid Luffman
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Raja Das: East Tennessee State University, USA
Arpita Nandi: East Tennessee State University, USA
Andrew Joyner: East Tennessee State University, USA
Ingrid Luffman: East Tennessee State University, Johnson City, USA

International Journal of Applied Geospatial Research (IJAGR), 2021, vol. 12, issue 1, 1-17

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jagr00:v:12:y:2021:i:1:p:1-17

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