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Landslide susceptibility mapping of Kalimpong in Eastern Himalayan Region using a Rprop ANN approach

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Abstract

Kalimpong district, a part of the Darjeeling Himalaya, exhibits a variety of factors that are ideal for the occurrence of landslides. Therefore, it is imperative to demarcate the zones that are highly susceptible to landslide phenomena in advance, so that the risk, and hence the damage can be reduced to a significant extent through proper land-use planning. The factors that have been considered for this study are: (1) elevation, (2) slope, (3) aspect, (4) curvature, (5) distance to drainage, (6) soil type, (7) rainfall, (8) distance to lineaments, (9) landuse, (10) distance to road, (11) TWI, and (12) NDVI. For landslide susceptibility mapping of Kalimpong district, a resilient back propagation (Rprop) artificial neural networks (ANN) approach was used in this study. The results of the Rprop ANN model were validated using the AUC of the ROC Curves. The prediction rate AUC value was found to be 84.35% which showed that this combination of factors with the Rprop ANN model gave satisfactory accuracy in agreement with past landslide phenomena. The derived landslide susceptibility map was categorized in extremely low, low, moderate, high, and very high susceptibility zones covering 610, 272, 83, 61, and 66.7 km2 of Kalimpong’s area, respectively.

Research highlights

  1. i)

    Landslide Susceptibility Mapping (LSM) of Kalimpong, Eastern Himalaya.

  2. ii)

    A new improvised approach to LSM using Resilient Back Propagation ANN.

  3. iii)

    Different combination of 12 landslide factors with Rprop ANN.

  4. iv)

    High Prediction accuracy of Rprop ANN Model at 84.35%.

  5. v)

    Quick and reliable LSM method for susceptible zone management.

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Authors and Affiliations

  1. Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India

    Pamir Roy

  2. Department of Mining Engineering, Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India

    Kaushik Ghosal & Prabir Kumar Paul

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  1. Pamir Roy
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  2. Kaushik Ghosal
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Contributions

Pamir Roy established the methodology, carried out the analysis, and prepared the images, tables, and the original manuscript. The data collection and pre-processing were done by Kaushik Ghosal. Prabir Kumar Paul reviewed and made corrections to the original manuscript.

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Correspondence to Pamir Roy.

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Communicated by Arkoprovo Biswas

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Roy, P., Ghosal, K. & Paul, P.K. Landslide susceptibility mapping of Kalimpong in Eastern Himalayan Region using a Rprop ANN approach. J Earth Syst Sci 131, 130 (2022). https://doi.org/10.1007/s12040-022-01877-2

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  • DOI: https://doi.org/10.1007/s12040-022-01877-2

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