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Earthquake vulnerability in the Himalaya by integrated multi-criteria decision models

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  • Sukanta Malakar

    (Indian Institute of Technology Kharagpur)

  • Abhishek K. Rai

    (Indian Institute of Technology Kharagpur)

Abstract

Himalayan mountains are one of the most seismo-tectonically active zones on the surface of the earth. Recurring moderate and high magnitude earthquakes are not uncommon in this region. This paper maps the earthquake vulnerability in the region using integrated multi-criteria decision models. Factors which may influence vulnerability in a region can be categorized in social, geotechnical, structural, and physical parameters. We have used the analytical hierarchy process (AHP) approach to determine the weights of various parameters, which were further used to develop earthquake vulnerability maps for the study area using the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Grey Relational Analysis (GRA) methods. There is good correlation between the vulnerability estimated through AHP-VIKOR and AHP-GRA methods. Our analysis indicates that more than 12% area may be under high to very-high vulnerability, whereas more than 44% population, and about 43% buildings are highly vulnerable to hazards due to earthquakes. The results would be useful for various hazard mitigation and infrastructure planning agencies working in the region.

Suggested Citation

  • Sukanta Malakar & Abhishek K. Rai, 2022. "Earthquake vulnerability in the Himalaya by integrated multi-criteria decision 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. 111(1), pages 213-237, March.
    • Handle: RePEc:spr:nathaz:v:111:y:2022:i:1:d:10.1007_s11069-021-05050-8
    DOI: 10.1007/s11069-021-05050-8
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    References listed on IDEAS

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    Cited by:

    1. Jiale Zhu & Yichen Zhang & Jiquan Zhang & Yanan Chen & Yijun Liu & Huanan Liu, 2023. "Multi-Criteria Seismic Risk Assessment Based on Combined Weight-TOPSIS Model and CF-Logistic Regression Model—A Case Study of Songyuan City, China," Sustainability, MDPI, vol. 15(14), pages 1-28, July.

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