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A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India

Author

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  • Tirthankar Basu

    (University of Gour Banga)

  • Swades Pal

    (University of Gour Banga)

Abstract

Landslide susceptibility map provides a useful tool to the decision-makers to prevent and mitigate landslide hazards. For this study 16 spatial parameters and past landslide inventory have been taken into consideration and these are categorized under six factors clusters. For providing relative importance to the parameters modified analytic hierarchy process is taken into consideration. Landslide susceptible zone (LSZ) is prepared compositing all those multiparametric spatial data layers. The obtained result shows that 7.80% area of total basin is highly susceptible for landslide. Correlation and regression analysis suggests that lithological factors cluster is the dominant one for determining very high LSZ. The validation shows that very high LSZ is associated with very high landslide frequency density. Besides this, receiver operating characteristics curve also shows 90.20% predicted area under the curve. So, this model can be treated as valid.

Suggested Citation

  • Tirthankar Basu & Swades Pal, 2020. "A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4787-4819, June.
    • Handle: RePEc:spr:endesu:v:22:y:2020:i:5:d:10.1007_s10668-019-00406-4
    DOI: 10.1007/s10668-019-00406-4
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    References listed on IDEAS

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    1. D. Kanungo & S. Sarkar & Shaifaly Sharma, 2011. "Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 59(3), pages 1491-1512, December.
    2. Hariklia D. Skilodimou & George D. Bathrellos & Efterpi Koskeridou & Konstantinos Soukis & Dimitrios Rozos, 2018. "Physical and Anthropogenic Factors Related to Landslide Activity in the Northern Peloponnese, Greece," Land, MDPI, vol. 7(3), pages 1-18, July.
    3. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    4. Majid Roodposhti & Saeed Rahimi & Mansour Beglou, 2014. "PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(1), pages 77-95, August.
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    Cited by:

    1. Amin Salehpour Jam & Jamal Mosaffaie & Faramarz Sarfaraz & Samad Shadfar & Rouhangiz Akhtari, 2021. "GIS-based landslide susceptibility mapping using hybrid MCDM models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 1025-1046, August.
    2. Zhiye Wang & Chuanming Ma & Yang Qiu & Hanxiang Xiong & Minghong Li, 2022. "Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China," IJERPH, MDPI, vol. 19(15), pages 1-22, August.
    3. Anik Saha & Sunil Saha, 2021. "Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 4453-4483, March.
    4. Yanrong Liu & Zhongqiu Meng & Lei Zhu & Di Hu & Handong He, 2023. "Optimizing the Sample Selection of Machine Learning Models for Landslide Susceptibility Prediction Using Information Value Models in the Dabie Mountain Area of Anhui, China," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    5. Mudahir Ozgul & Turgay Dindaroglu, 2021. "Multi-criteria analysis for mapping of environmentally sensitive areas in a karst ecosystem," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16529-16559, November.
    6. Hua Wang & Qing Guo & Xiaoqing Ge & Lianzi Tong, 2022. "A Spatio-Temporal Monitoring Method Based on Multi-Source Remote Sensing Data Applied to the Case of the Temi Landslide," Land, MDPI, vol. 11(8), pages 1-19, August.

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