Landslide analysis in Geographic Information Systems
Hazard mapping of landslides
Landslide hazard maps have been constructed by different methods, such as, from inventories; by consideration of site conditions including geology, hydrology, topography, and/or geomorphology; by statistical correlation of landslide frequency with geologic and geomorphic factors; using safety factors from stability analysis. Mechanics based models have also been used to estimate failure probability based on uncertainties about infiltration of rainfall, or pore pressure, and soil strength (Wu et al. 2000). An a priori study of causal factors of landslides indicate, the ground conditions, geomorphological processes, physical processes and man-made processes as the significant contributors. The pertaining data needed for landslide analysis for a particular slope are:
As part of the research project, a landslide at Mangalore, Karnataka is in investigation. A slope stability analysis combined with risk analyses is proposed to carry out. The area under investigation is shown in Figure 1, marked with 100 point elevations and contours of 2m interval. It could observed that the hill is sloping downward towards southeast. The slide has occurred at the upper regions and hence short-term remediation in the form of relief wells and reinforcing piles were employed at appropriate locations. Various man-made features are also present, like roads and drainage structures, a stream falling in the downslope. However, the theoretical framework for the hazard mapping of the slide area presented has been done in detail. The steps involved in the landslide hazard mapping are given in Figure 2 as flow chart. The detailed formulation of the work is described below. Further, an illustration with a hypothetical slope for simulated soil and earthquake conditions is demonstrated at the end. The methodology proposed would be able to develop the hazard maps, incorporating the triggering causal factors, viz., both rainfall and earthquakes. Site conditions The ground surface elevations could be taken from the topographic maps or digital elevation models (DEM). The other input parameters for the predictive model, involves the detailed characterization of the soil conditions, viz., the description of soil strength properties (cohesion, angle of internal friction, unit weight), permeability characteristics, depth of soil cover, vegetation pattern. The soil cover depth is important in case of shallow soil sliding, which occurs prevalently. The presence of vegetation covers influences the stability calculations significantly as the forested area has increased cohesion value of the soil than the clear-cut areas. Pertinent literature shows that forested areas has upto 20% increased cohesion value (Wu et al. 2000). The values of strength properties would eventually vary spatially throughout the slope. If considerable effort is paid in detailed site investigation, in determining the strength values at more number of points, then a separate statistical analysis (Geostatistics or random field analysis) could be performed, to evaluate the correlation distance. With the information of the correlation distance of each property varying in both vertical and horizontal directions, the interpolation of strength values at unsampled points could be done in Arc View through the Kriging operation. The Arc View facilitates the user to write an avenue coding through which this operation could be performed.  Fig. 2: Formulation of landslide hazard mapping The geostatistical interpolation is more accurate than the available IDW method or spline methods of interpolation, especially for the geological properties. |