Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada

Zhaohua Chen () and Jinfei Wang

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2007, vol. 42, issue 1, 75-89

Abstract: A logistic regression model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards in a mountainous environment. A case study is conducted in the mountainous southern Mackenzie Valley, Northwest Territories, Canada. To determine the factors influencing landslides, data layers of geology, surface materials, land cover, and topography were analyzed by logistic regression analysis, and the results are used for landslide hazard mapping. In this study, bedrock, surface materials, slope, and difference between surface aspect and dip direction of the sedimentary rock were found to be the most important factors affecting landslide occurrence. The influence on landslides by interactions among geologic and geomorphic conditions is also analyzed, and used to develop a logistic regression model for landslide hazard mapping. The comparison of the results from the model including the interaction terms and the model not including the interaction terms indicate that interactions among the variables were found to be significant for predicting future landslide probability and locating high hazard areas. The results from this study demonstrate that the use of a logistic regression model within a GIS framework is useful and suitable for landslide hazard mapping in large mountainous geographic areas such as the southern Mackenzie Valley. Copyright Springer Science+Business Media, Inc. 2007

Keywords: Landslide hazard mapping; Geographic Information Systems; Logistic regression; Multivariate analysis (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s11069-006-9061-6

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