Landslide Susceptibility Zonation Mapping: A Case Study from Darjeeling District, Eastern Himalayas, India

  • Amit ChawlaEmail author
  • Srinivas Pasupuleti
  • Sowmiya Chawla
  • A. C. S. Rao
  • Kripamoy Sarkar
  • Rajesh Dwivedi
Research Article


Landslides have been one of the most damaging natural hazards in the hilly region, which cause loss of life and infrastructure, and hence, landslide susceptibility zonation (LSZ) maps are inevitable for the pre-identification of vulnerable slopes and for the future planning and mitigation programmes. In this study, an integrated remote sensing and geographic information system approach is adopted for the generation of LSZ Map for the Darjeeling and Kalimpong district, West Bengal, India. Topographic maps, satellite data, other informative maps and statistics were utilized. For this study, the causative factors which cause instability of slope such as drainage, lineament, slope, rainfall, earthquake, lithology, land use, geomorphology, soil, aspect and relief were considered. For the generation of LSZ map, thematic data layers were evaluated and generated by assigning appropriate numerical values for each factor weight and their corresponding class rating in the GIS environment. Resulting LSZ map outlines the total study area into five different susceptibility classes: very high, high, moderate, low and very low. This study also demonstrates the classification and prediction of landslide-susceptible zones in coalition with GIS output by using particle swarm optimization–support vector machine approach without feature selection and ant colony optimization approach with feature selection along with support vector machine classifier. GIS-based LSZ map was validated by comparing the landslide frequencies in between the susceptible classes. The usefulness of the LSZ map was also validated by the statistical Chi-square test.


Landslide susceptibility zonation (LSZ) Geographic information system (GIS) Remote sensing (RS) 



The author(s) thank Centre for Seismology, New Delhi, India, Survey of India, Kolkata, Geological Survey of India, Kolkata, India, National Atlas and Thematic Mapping Organisation, Kolkata, India, and National Bureau of Soil Survey and Land Use Planning, Kolkata, India, etc. for providing various data used in this study. The author(s) are grateful to Mr. Harish Sinha, CHiPS, India, and Dr. Shantanu Sarkar, CBRI, Roorkee, India, for their valuable suggestions. Support from the Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India, Public Works Department, Delhi, India, and Central Public Works Department, Delhi, India, is also acknowledged.

Compliance with Ethical Standards

Conflict of interest

The author(s) declare that they have no conflict of interest.


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Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Technology (Indian School of Mines)DhanbadIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology (Indian School of Mines)DhanbadIndia
  3. 3.Department of Applied GeologyIndian Institute of Technology (Indian School of Mines)DhanbadIndia
  4. 4.Department of Computer Science and EngineeringVignan’s Foundation for Science, Technology and ResearchVadlamudiIndia

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