Monday, 6 October 2008: 4:00 PM
George R. Brown Convention Center, 350DEF
Mass movements (MM) are probably the most damaging to the natural and human environment in the Mediterranean countries, including Lebanon representing a good case study of mountainous landscape. Although affecting vast areas in the country, the phenomenon was not studied at regional scale, and related maps are still lacking. Therefore, this paper deals with a newly proposed mathematical decision making method Valuing Analytical Bi-Univariate (VABU) that considers two-level weights for mapping MM susceptibility/hazard (1:50,000 cartographic scale). This mapping method was applied on a study area that spreads over the central and northern parts of the Lebanese territory covering 3750 km2 (36% Lebanese territory). MM susceptibility is obtained through the integration of thirteen parameters considered as preconditioning factors governing the stability conditions of the terrain (elevation, slope gradient, lithology, land cover/use, etc...). Whereas, MM hazards is determined by integrating these thirteen preconditioning parameters and the four triggering ones, i.e. rainfall quantity, seismic events, floods and forest fires. The reliability of this method is examined through field surveys and depending on a GIS comparison with other statistical methods commonly used worldwide Valuing accumulation Area (VAA) and Information Value (InfoVal). Three susceptibility maps were derived using preconditioning parameters, while hazard maps were produced from triggering ones. The coincidence values of overlapping susceptibility maps were found to be equal to 47.5% (VABU/VAA), 54% (VABU/InfoVal) and 38% (VAA/InfoVal). The agreement between hazard maps showed closer values than susceptibility ones, oscillating between 36.5% (VAA/InfoVal), 39% (VABU/VAA), and 44% (VABU/InfoVal). Field verification indicates that the total precision of the produced susceptibility maps ranges from 52.5% (VAA method), 67.5% (InfoVal method), and 77.5% (VABU method). This demonstrates the efficiency of our method, which consequently can be adopted for predictive mapping of MM susceptibility/hazard in other areas and may be easily extrapolated using the functional capacities of GIS.
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