Natural Hazards

, Volume 65, Issue 1, pp 315–330 | Cite as

Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India

  • D. Ramakrishnan
  • T. N. Singh
  • A. K. Verma
  • Akshay Gulati
  • K. C. Tiwari
Original Paper


This paper mainly presents a case study of landslide vulnerability zonation along Tawaghat-Mangti route corridor in Kumaon Himalaya, India. An attempt is made to predict landslide susceptibility using back-propagation neural network (BPNN) and propose a suitable model for that zone, which can be successfully implemented for the prevention of slides. Various landslide affecting parameters such as lithology, slope, aspect, structure, geotechnical properties, land use, landslide inventory, and distance from recorded epicenter are used to model the landslide susceptibility. The database on the above parameters derived from satellite imageries, topographic maps, and field work are integrated in the GIS to generate an information layer. Database of this information layer is used to train, test, and validate the BPNN model. A three-layered BPNN with an input layer, two hidden layers, and one output layer is found to be optimal. The developed model demonstrates a promising result, and the prediction accuracy has been found to be 80 % in the field.


Kumaon Himalaya Landslide susceptibility GIS BPNN 


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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • D. Ramakrishnan
    • 1
  • T. N. Singh
    • 1
  • A. K. Verma
    • 1
  • Akshay Gulati
    • 1
  • K. C. Tiwari
    • 2
  1. 1.Department of Earth SciencesIndian Institute of TechnologyMumbaiIndia
  2. 2.Department of GeologyM.S. University of BaordaVadodaraIndia

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