Spatial three-dimensional landslide susceptibility mapping tool and its applications

Abstract

There are three methods of zoning landslide susceptibility: qualitative, statistical methodologies, and geotechnical model. Qualitative approaches are based on the judgment of those conducting the susceptibility or hazard assessment; the statistical approach uses a predictive function or index derived from a combination of weighted factors; and the deterministic, or physically based, models are based on the physical laws of conservation of mass, energy, and momentum. Two-dimensional deterministic models are widely used in the design of civil engineering, and the infinite slope model (one-dimensional) is always employed in the deterministic-model-based landslide hazard mapping. This article presents a new GIS (Geographic Information Systems)-based landslide susceptibility mapping system which can be used to identify the three-dimensional (3-D) landslide bodies from complex topography. All slope-related spatial information (vector or raster dataset) was integrated in the system, by dividing the study area into slope units and assuming the initial slip to be the lower part of an ellipsoid. The 3-D critical slip surface in the 3-D slope stability analysis was located by minimizing the 3-D safety factor using the Monte Carlo random simulation. The failure probability of the landslide was calculated using an approximate method in which effective cohesion, effective friction angle, and 3-D safety factor were assumed to be in normal distribution. A computational program called 3-DSlopeGIS, in which a GIS Developer kit (ArcObjects of ESRI) had been used to fulfill the GIS spatial analysis function and effective data management, has been developed to implement all the calculations of the 3-D slope problem. By using the spatial analysis functions, the data management, and the visualization of GIS for processing the complicated slope-related data, the 3-D slope stability problem is easier to be studied through a friendly visual graphical user interface. The system has been applied for mapping the landslide susceptibility of three examples: the first one for city planning, the second for predicting the possible landslide influence around a past slope disaster, and the third for mapping landslide along a national route. Based on numerous Monte Carlo simulation, the possible critical landslide bodies have been identified, which cannot be carried out by using the traditional slope stability analyses.