Landslide Susceptibility Zonation through ratings derived from Artificial Neural Network |
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| Authors: | Shivani Chauhan Mukta Sharma MK Arora NK Gupta |
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| Institution: | 1. Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India;2. Department of Geology, Delhi University, Delhi, India;3. Institute Computer Center, Indian Institute of Technology Roorkee, Roorkee, India |
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| Abstract: | In the present study, Artificial Neural Network (ANN) has been implemented to derive ratings of categories of causative factors, which are then integrated to produce a landslide susceptibility zonation map in an objective manner. The results have been evaluated with an ANN based black box approach for Landslide Susceptibility Zonation (LSZ) proposed earlier by the authors. Seven causative factors, namely, slope, slope aspect, relative relief, lithology, structural features (e.g., thrusts and faults), landuse landcover, and drainage density, were placed in 42 categories for which ratings were determined. The results indicate that LSZ map based on ratings derived from ANN performs exceedingly better than that produced from the earlier ANN based approach. The landslide density analysis clearly showed that susceptibility zones were in close agreement with actual landslide areas in the field. |
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| Keywords: | Artificial Neural Network Landslide susceptibility Remote sensing |
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