Year of Award

2022

Document Type

Thesis

Degree Type

Master of Science (MS)

Degree Name

Geosciences

Department or School/College

Geosciences

Committee Chair

Rebecca Bendick

Commitee Members

Andrew Wilcox, Kevin McManigal

Keywords

landslide susceptibility assessment, logistic regression, frequency ratio, model transferability

Publisher

University of Montana

Subject Categories

Applied Statistics | Geomorphology | Statistical Models

Abstract

Landslides are a globally pervasive problem with the potential to cause significant fatalities and economic losses. Although landslides are widespread, many at-risk regions may not have the high-quality data or resources used in most landslide susceptibility analyses. This study aims to develop regional susceptibility relationships that are versatile and use publicly available data and open-sourced software. Logistic Regression and Frequency Ratio susceptibility relationships were developed in 23 regions in Washington, Utah, North Carolina, and Kentucky, with a region referring to a unique area and data combination. Regions were diverse in their geology, morphology, climate, and nature and quality of their landslide data. The transferability of select models to regions uninvolved in model development was also tested. The transferred models were trained using data from a single region (single-region cross-validation) or a combination of regions (multi-region cross-validation). Potential landslide contributing factors were all derived from a globally available digital surface model while landslide inventories were publicly available from state geological surveys. The contributing factors considered were elevation, slope, aspect, planform curvature, profile curvature, and topographic position index. Models developed using high-quality landslide data delineating scarps, flanks, and individual slope movements performed very well (AUC 0.764 - 0.895; AUC = area under relative operating characteristics curve). Models developed using landslide data dominated by deposits performed less well, but at or near an acceptable level (AUC 0.67 – 0.81). Models developed using older, lower quality landslide data did not perform at an acceptable level (AUC 0.63 – 0.64). The results of testing model transferability had acceptable results for some but not all regions (AUC 0.563 - 0.844). This study is a promising first step in developing generalized landslide susceptibility relationships that can be used in areas that share similar regional scale attributes.

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© Copyright 2022 Gina M. Belair