GIS and AHP-Based Landslide Susceptibility Mapping of the Teesta River Basin, Eastern Darjeeling-Kalimpong Himalaya, India

Authors

  • Kankona Maity Techno India University, West Bengal, India
  • Sangita Hazra Techno India University, West Bengal, India

DOI:

https://doi.org/10.66132/ngce20260205

Keywords:

Landslide, Analytic Hierarchy Process (AHP), Hazard Mapping, Geographic Information System (GIS), Teesta River Basin

Abstract

Landslides are among the most recurrent hydro-geomorphic hazards in the Eastern Himalaya, where steep terrain, weak lithology, intense monsoonal rainfall, high drainage dissection and increasing anthropogenic pressure collectively reduce slope stability. This study prepared a landslide susceptibility map for a part of the Teesta River Basin covering Darjeeling and Kalimpong districts, West Bengal, India, using Geographic Information System (GIS), Remote Sensing (RS) and the Analytic Hierarchy Process (AHP). Seventeen landslide-conditioning factors were incorporated: altitude, slope, aspect, curvature, relative relief, lineament density, distance from lineament, land use/land cover, soil, Normalized Difference Vegetation Index, lithology, geomorphology, drainage density, rainfall, Topographic Wetness Index, distance from drainage and Stream Power Index. Thematic layers were derived from DEM, satellite data, geological and soil information, rainfall data and ancillary spatial datasets, standardized in a GIS environment and integrated through weighted overlay analysis. The resulting susceptibility map classified the basin into very low, low, moderate, high and very high susceptibility zones. Moderate susceptibility occupied the largest area (69.85 km²), followed by high susceptibility (68.74 km²), low susceptibility (48.94 km²), very high susceptibility (37.87 km²) and very low susceptibility (19.48 km²). The northern and central mountainous sectors, especially around Kalimpong and Kalimpong-I, were more susceptible than the southern foothill areas. The map provides a spatially explicit decision-support layer for land-use planning, infrastructure alignment, disaster risk reduction and sustainable watershed management in the Teesta Himalayan landscape.

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References

Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, central Japan. Geomorphology, 65(1-2), 15-31. https://doi.org/10.1016/j.geomorph.2004.06.010 DOI: https://doi.org/10.1016/j.geomorph.2004.06.010

Bachri, S., Shrestha, R. P., Utaya, S., Sumarmi, Prastiwi, M., Putri, N., & Hidiyah, T. (2025). Landslide susceptibility assessment through bivariate models (weight of evidence and frequency ratio) in Pesanggaran, East Java, Indonesia. Geoenvironmental Disasters, 12(1), Article 41. DOI: https://doi.org/10.1186/s40677-025-00345-5

Bonham-Carter, G. F. (1994). Geographic information systems for geoscientists: Modelling with GIS. Pergamon Press.

Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modeling using GIS, LiDAR, and probabilistic methods. Geomorphology, 42(3-4), 213-228. https://doi.org/10.1016/S0169-555X(01)00087-3 DOI: https://doi.org/10.1016/S0169-555X(01)00087-3

Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology, 102(3-4), 85-98. https://doi.org/10.1016/j.enggeo.2008.03.022 DOI: https://doi.org/10.1016/j.enggeo.2008.03.022

Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112(1-2), 42-66. https://doi.org/10.1016/j.earscirev.2012.02.001 DOI: https://doi.org/10.1016/j.earscirev.2012.02.001

Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1-4), 272-299. https://doi.org/10.1016/j.geomorph.2005.06.002 DOI: https://doi.org/10.1016/j.geomorph.2005.06.002

Kayastha, P., Dhital, M. R., & De Smedt, F. (2013). Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal. International Journal of Geomatics and Geosciences, 3(3), 595-610. DOI: https://doi.org/10.1016/j.cageo.2012.11.003

Kundu, S., & Ghosh, S. (2025). GIS-integrated landslide susceptibility mapping in the Teesta River Basin of the Darjeeling-Sikkim Himalaya: A comparative analysis of frequency ratio, logistic regression models, and analytical hierarchy process. In Progress in multicriteria decision making models: A new paradigm to monitor hazards (pp. 131-169). Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-89246-2_7

Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1), 33-41. https://doi.org/10.1007/s10346-006-0047-y DOI: https://doi.org/10.1007/s10346-006-0047-y

Mandal, B., Biswas, B., & Mandal, S. (2026). Application of bivariate statistical models to explore landslide susceptibility and risk in the Lish River basin of Darjeeling Himalaya. Environment, Development and Sustainability, 28(4), 8225-8274. DOI: https://doi.org/10.1007/s10668-024-05303-z

Mandal, B., Mondal, S., & Mandal, S. (2023). GIS-based landslide susceptibility zonation (LSZ) mapping of Darjeeling Himalaya, India using weights of evidence (WoE) model. Arabian Journal of Geosciences, 16(7), Article 421. DOI: https://doi.org/10.1007/s12517-023-11523-w

Pitchaimani, V. S., Joe, R. J., Stuvar, S. R., Liberthin, M., Promilton, A. A. A., & Abishek, S. R. (2026). Landslide susceptibility and vulnerability assessment using GIS-AHP in the highland of Wayanad, Western Ghats, India. Discover Hazards, 2(1), Article 5. DOI: https://doi.org/10.1007/s44475-026-00008-5

Poddar, I., & Roy, R. (2024). Application of GIS-based data-driven bivariate statistical models for landslide prediction: A case study of highly affected landslide prone areas of Teesta River basin. Quaternary Science Advances, 13, Article 100150. DOI: https://doi.org/10.1016/j.qsa.2023.100150

Pradhan, B., & Lee, S. (2010). Regional landslide susceptibility analysis using GIS, remote sensing, and statistical models in the mountainous regions. Environmental Earth Sciences, 61(2), 215-230.

Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically based landslide susceptibility models. Earth-Science Reviews, 180, 60-91. https://doi.org/10.1016/j.earscirev.2018.03.001 DOI: https://doi.org/10.1016/j.earscirev.2018.03.001

Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill. DOI: https://doi.org/10.21236/ADA214804

Sarkar, S., Kanungo, D. P., Patra, A. K., & Kumar, P. (2013). GIS-based landslide susceptibility mapping in the Indian Himalaya. Natural Hazards, 68(2), 839-861.

Van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3-4), 112-131. https://doi.org/10.1016/j.enggeo.2008.03.010 DOI: https://doi.org/10.1016/j.enggeo.2008.03.010

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Published

2026-06-30

Data Availability Statement

Data are available from the corresponding author upon reasonable request.

How to Cite

Maity, K. ., & Hazra, S. . (2026). GIS and AHP-Based Landslide Susceptibility Mapping of the Teesta River Basin, Eastern Darjeeling-Kalimpong Himalaya, India. NG Civil Engineering, 2(2), 52-63. https://doi.org/10.66132/ngce20260205

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