Application of GIS-Based Knowledge-Driven and Data-Driven Methods for Debris-Slide Susceptibility Mapping
Das R, Nandi A, Joyner A, Luffman I. 2021. Application of GIS-Based Knowledge-Driven and Data-Driven Methods for Debris-Slide Susceptibility Mapping. International Journal of Applied Geospatial Research. 12(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.
- Type
- Journal Article
- Authors
- Das, Raja; Nandi, Arpita; Joyner, Andrew; Luffman, Ingrid
- Units
- GRSM
- Keywords
- Anakeesta, data-driven method, debris-slide, frequency ratio, GIS, GRSM-01169, knowledge-driven method, mass movement, receiver operating characteristic curve, surficial geology, susceptibility, weighted overlay