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Coseismic landslide hazard modeling methodologies - 1. Introduction

 

1. Introduction

Coseismic landslides are the phenomena resulting from an earthquake that produced ground accelerations that exceeded the threshold necessary to initiate slope failure (Newmark, 1965).

Coseismic landslide events are the most significant secondary hazard of earthquakes in areas of high relief, and can be responsible for high fatality rates (Marano et al., 2009). These types of slope failures result in detrimental economic and human loss because they displace massive volumes of rock and soil across tens of thousands of km2 (Keefer, 1994). For example, the 2008 M7.9 Wenchuan earthquake triggered more than 56,000 landslides, which contributed significantly to the loss of nearly 70,000 lives (Dai et al., 2011). Rapid assessment of hillslope failure is essential for aiding disaster relief teams in recovery and reconstruction after earthquakes (Marano et al., 2009).

Landslide susceptibility assessment, both spatially (landslide susceptibility in a region) and temporally (landslide hazard over a certain time period), aids in land use development and emergency planning in an area (e.g. Brenning, 2005; Nowicki, 2011). Generally, assessment methods incorporate multiple variables that contribute to landslide susceptibility, including past slope failures, environmental conditions, geologic and geotechnical parameters, and other phenomena associated with an area (e.g. Keefer, 2002; Dia et al., 2011).

Landslide hazard assessment has been broadly studied and many methods have been developed; yet, not one has been proven the most universally effective for hazard mapping. Methodology includes qualitative, semi-quantitative, quantitative, deterministic and probabilistic assessment. Common techniques to modeling include mechanical modeling and statistical analysis. Physical techniques, such as the Newmark (1965) sliding block model, rely on simple mechanical laws rather than complete landslide inventories and data (Pardeshi et al., 2013). Common statistical approaches include: logistic regression, artificial neural networks (ANN), discriminant analysis, support vector machines, and bootstrap-aggregated classification trees (Brenning, 2005). Statistical approaches to landslide hazard modeling are preferable because they minimize subjectivity in weightage assignment procedure, producing more objective and reproducible results (Kangungo et al., 2009). This project takes an in depth look into three case studies, focusing on statistical (logistic regression and artificial neural networking) and physical (capacity-demand) modeling of landslide hazard, both regionally and globally.

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