Comparison Between Statistical Models: A Review and Evaluation

  • Sujit Mandal
  • Subrata Mondal


The development of various models and their application in studies have brought a significant change in the subject discipline of geography. In the present study, various geomorphic and geohydrologic parameters, 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, and their integration was made on GIS environment to prepare landslide susceptibility zonation map of Darjeeling Himalaya, India. To generate all data layers, Google earth imagery, toposheet and GPS field survey data (2015–2016); geological and soil map; SRTM DEM (30 m spatial resolution); Landsat TM Image, Feb. 2009 (30 m spatial resolution), rainfall data (1950–2010) and some other information were processed with the help of GIS. To integrate all the data layers and to prepare landslide susceptibility map, several models such as frequency ratio (FR) model, modified information value (MIV) model, logistic regression (LR) model, artificial neural network (ANN) model, weighted overlay analysis (WOA) model, certainty factor (CF) model, analytical hierarchy process (AHP) model and fuzzy logic (FL) approach were applied. The prepared landslide susceptibility maps using all the models were classified into five, i.e. very low, low, moderate, high, and very high. All the developed landslide susceptibility maps of Darjeeling Himalaya were being validated using receiver operating characteristics (ROC) curve). The study concluded that artificial neural network model (ANN), certainty factor (CF) model, and frequency ratio-based fuzzy logic approach are most reliable statistical techniques in the assessment and prediction of landslide susceptibility in Darjeeling Himalaya because of high level of accuracy in comparison to models applied in the study.


Landslide susceptibility Statistical models Models validation and comparison 


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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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