Mitigation: Mechanics, Monitoring, Modeling, and Assessment - Proceedings of the 7th International Conference on Debris-Flow Hazards Mitigation. All rights reserved. Debris-slides are a frequent hazard in fragile decomposed metasedimentary rocks in the Anakeesta rock formation in Great Smoky Mountain National Park. The spatial distribution of an existing debris-slide area could be used to prepare susceptibility map for future debris-slide initiation zones. This work aims to create a debris-slide susceptibility map by using a knowledge-driven method in a GIS platform in Anakeesta formation of Great Smoky Mountain National Park. Six geofactors, namely, elevation, annual rainfall, slope curvature, landcover, soil texture and various slope failure modes were used to create the susceptibility map. Debris-slide locations were mapped from the satellite imagery, previous studies, and field visits. A Weighted Overlay Analysis was performed in order to generate the final susceptibility map, where individual classes of geofactors were ranked and were assigned weights based on their influence on debris-slide. The final susceptibility map was classified into five categories: very low, low, moderate, high and very high susceptible zones. Validation of the result shows very high category predicted ~10%, high and moderate categories predicted 75.5% and ~14.5% of the existing debris-slide pixels respectively. This study successfully depicts the advantage and usefulness of the knowledge-driven method, which can save considerable amount of time and reduce complicated data analysis unlike statistical or physical based methods. However, the accuracy of the model highly depends on the researcher’s experience of the area and selection of respective geofactors.

Debris-slides are one of the most frequently occurring geological hazards in metasedimentary rocks of the Anakeesta ridge in Great Smoky Mountains National Park (GRSM), which often depends on the influence of multiple causing factors or geo-factors such as geological structures, slope, topographic elevation, land use, soil type etc. or a combination of these factors. The main objective of the study was to understand the control of geo-factors in initiating debris-slides using different knowledge and data-driven methods in GIS platform. The study was performed in three steps: (1) Evaluation of geometrical relationship between geological discontinuity and topographic orientation in initiation of debris-slides, (2) Preparation of knowledge-driven debris-slide susceptibility model, and (3) Preparation of data-driven debris-slide susceptibility models and compare their efficacy. Performance of the models were evaluated mostly using area under Receiver Operating Characteristic (ROC) curve, which revealed that the models were statistically significant.

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 geotechnical investigation.