Application of The Factor-Based Approach to Evaluate Landslide Hazard in The Shiv-Khola Watershed of Darjiling Himalaya
DOI:
https://doi.org/10.24906/isc/2013/v27/i2/177580Keywords:
Landslide, Landslide Potentiality Index Value (LPIV), Landslide Susceptibility Index Value (LSIV), Landslide Zonation, Co-Efficient of Determination (R).Abstract
The quantitative analysis of landslide inducing attributes like slope, amplitude of relief, drainage density, upslope contributing area, topographic index, and land use is of great significance for the scientific management of mountain watershed. Preparation of Landslide Zonation Map is an important technique which figure out spatial distribution of landslide and helps to take site specific proper remedial measures in a rational manner. In the present study the interaction of different factors are studied separately and ultimately final coordination is made through Landslide Potentiality Index Value (LPIV) and Landslide Susceptibility Index Value (LSIV). For the preparation of the hazard zonation map of the Shivkhola watershed, grid/cell wise weighted index value (WIV) is assigned for each and every classes of individual attributes on the basis of the magnitude of landslide potentiality index value. Landslide Susceptibility Index Value (LPIV) is the outcome of the cumulative total of all grid/cell wise assigned WIV. Lastly, a statistical analysis has been made using Origin Software (8.00) to bring out the relationship between LSIV (Y) and individual landslide inducing factors (X).-The analysis states that 97.78,99.36,99.0 9 ,9 8 .9 7 ,9 9 .0 4 ,9 7 .5 0 , 96.61 and 98.63 percent of the total variation in LSIV (Y) is already being explained by X variables of slope, relief, drainage, constant of channel maintenance, road contributing area, upslope contributing area, topographic index and land use and a considerable percentages (2.22,0.64, 0.91,1.03, 3.03,0.96, 2.50, 3.39 and 1.37) of variations in Y are yet to be explained. On an average 98.21% of Y variable is being explained by the corresponding X one. So, the result of R square shows that the independent variables of X (factors) taken here is giving a good explanation of Y (LSIV) variables.
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