Rainfall-Induced Landslide Susceptibility Using a Rainfall–Runoff Model and Logistic Regression

Abstract

Conventional landslide susceptibility analysis adopted rainfall depth or maximum rainfall intensity as the hydrological factor. However, using these factors cannot delineate temporal variations of landslide in a rainfall event. In the hydrological cycle, runoff quantity reflects rainfall characteristics and surface feature variations. In this study, a rainfall–runoff model was adopted to simulate the runoff produced by rainfall in various periods of a typhoon event. To simplify the number of factors in landslide susceptibility analysis, the runoff depth was used to replace rainfall factors and some topographical factors. The proposed model adopted the upstream area of the Alishan River in southern Taiwan as the study area. The landslide susceptibility analysis of the study area was conducted by using a logistic regression model. The results indicated that the overall accuracy of predicted events exceeded 80%, and the area under the receiver operating characteristic curve (AUC) closed to 0.8. The results revealed that the proposed landslide susceptibility simulation performed favorably in the study area. The proposed model could predict the evolution of landslide susceptibility in various periods of a typhoon and serve as a new reference for landslide hazard prevention.

Investigation of the Influence of Rainfall Runoff on Shallow Landslides in Unsaturated Soil Using a Mathematical Model

“…On the basis of the concept of the effective stress, the shear strength of the soil can be written using the extended Mohr-Coulomb failure criterion [8] as: (12) where τ denotes the shear strength of the soil; c is the effective cohesion; φ represents the angle of effective friction; σ is the total normal stress; u a and u w are the pore air pressure and pore water pressure, respectively; u a − u w denotes the matric suction; and χ denotes the effective stress parameter dependent on the degree of saturation. Much experimental evidence has shown that the effective stress parameter χ of unsaturated soil is a highly nonlinear function of the matric suction.…”

“…where S is the saturation degree, S r denotes the residual degree of saturation, and S e represents the effective saturation. From Equation (12) and the water retention curve (i.e., Equation (2)), a highly nonlinear relationship between the effective stress parameter and the matric suction can be observed for unsaturated soil. Equations (12) and (14) show that the unsaturated shear strength is related to the saturation degree.…”

“…Soil failure occurs at a depth Z when the acting stress is greater than the resisting stress (i.e., the stress required to resist friction and cohesion). By adopting the infinite slope stability analysis with the shear strength of soil, as given by Equation (12), and assuming the pore air pressure is atmospheric, the factor of safety (FS) [7] can be expressed as where γ w denotes the unit weight of water and γ denotes the depth-averaged unit weight of soil, which can be written as (16) where G S is the specific gravity of soil solids. In Equation (15), if the soil is saturated (i.e., the groundwater pressure head is positive), ψ P is identical to ψ and ψ c is zero; if the soil is unsaturated, ψ c is equal to ψ, whereas ψ P is zero.…”

“…The details of the algorithm of the 2D-SWE module can be found in [23], and details of the soil failure module can be found in [11] or [7]. An iterative procedure ( Figure 1) is adopted to solve Equations (1) to (12). The iterative approach alternates the solutions of the infiltration and shallow water equations at each time step until convergence is reached.…”

Dynamic Modeling of Sediment Budget in Shihmen Reservoir Watershed in Taiwan

“…Logistic regression analysis, also called binary regression analysis, is a widely used statistical model when the dependent variable is binary, e.g., landslide occurrence or nonoccurrence, and independent variables are in the numerical type (e.g., slope), in the categorical type (e.g., lithology), or in both types (e.g., [44]). As it is beyond the present paper's scope, some detailed verification of applying the logistic regression model to landslide prediction can be referred to in the literature (e.g., [45][46][47]).…”