4.7 Article

Assessment of shallow landslide susceptibility using the transient infiltration flow model and GIS-based probabilistic approach

Journal

LANDSLIDES
Volume 13, Issue 5, Pages 885-903

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s10346-015-0646-6

Keywords

GIS; Infinite slope model; Transient infiltration model; Monte Carlo simulations; ROC

Funding

  1. National Research Foundation of Korea (NRF) - Korean government (MOE) [NRF-2013R1A1A2A10058724]
  2. National Research Foundation of Korea [2013R1A1A2A10058724] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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This study proposes a probabilistic analysis method for modeling rainfall-induced shallow landslide susceptibility by combining a transient infiltration flow model and Monte Carlo simulations. The spatiotemporal change in pore water pressure over time caused by rainfall infiltration is one of the most important factors causing landslides. Therefore, the transient infiltration hydrogeological model was adopted to estimate the pore water pressure within the hill slope and to analyze landslide susceptibility. In addition, because of the inherent uncertainty and variability caused by complex geological conditions and the limited number of available soil samples over a large area, this study utilized probabilistic analysis based on Monte Carlo simulations to account for the variability in the input parameters. The analysis was performed in a geographic information system (GIS) environment because GIS can deal efficiently with a large volume of spatial data. To evaluate its effectiveness, the proposed analysis method was applied to a study area that had experienced a large number of landslides in July 2006. For the susceptibility analysis, a spatial database of input parameters and a landslide inventory map were constructed in a GIS environment. The results of the landslide susceptibility assessment were compared with the landslide inventory, and the proposed approach demonstrated good predictive performance. In addition, the probabilistic method exhibited better performance than the deterministic alternative. Thus, analysis methods that account for uncertainties in input parameters are more appropriate for analysis of an extensive area, for which uncertainties may significantly affect the predictions because of the large area and limited data.

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