Artificial Neural Network (ANN) Model and Landslide Susceptibility

  • Sujit Mandal
  • Subrata Mondal


The present study is dealt with the preparation of landslide susceptibility map of Darjeeling Himalaya with the help of GIS tools and artificial neural network (ANN) model. Fifteen landslide causative factors, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were considered to produce the landslide susceptibility zonation map. To generate all these aforesaid causative factors map, topographical maps, geological map, soil map, satellite imageries, Google earth images and some other authorized maps were processed and constructed into a spatial data base using GIS and image processing techniques. The back-propagation method was applied to estimate factor’s weight and the landslide hazard indices were derived with the help of trained back-propagation weights. Then, the landslide susceptibility zonation map of Darjeeling Himalaya was made using GIS tool and classified into five, i.e. very low, low, moderate, high, and very low landslide susceptibility. To validate the prepared landslide susceptibility map, landslide inventory was used and accuracy result was obtained after processing ROC curve. The accuracy of the landslide susceptibility map was 81.5% which is desirable.


Landslide susceptibility Artificial neural network (ANN) GIS tool ROC curve 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Sujit Mandal
    • 1
  • Subrata Mondal
    • 2
  1. 1.Department of GeographyDiamond Harbour Women’s UniversitySarishaIndia
  2. 2.University of Gour BangaMokdumpurIndia

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