Application of Knowledge-Driven Method for Debris-Slide Susceptibility Mapping in Regional Scale

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Conference Proceeding

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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.