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Seismicity clusters and vulnerability in the Himalayas by machine learning and integrated MCDM models

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Abstract

We use machine learning to identify spatial clusters of earthquakes, besides using multi-criteria decision-making models (MCDM) to estimate earthquake vulnerability in the Himalayan seismo-tectonic zone. Seismicity data from the USGS catalogue having 7687 earthquakes indicate eight active source zones or clusters of earthquakes. The earthquake clusters located in the eastern Himalayas are relatively larger in areal extent than those in the western Himalayas. The vulnerability was assessed using two MCDM models, i.e. fuzzy analytical hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS). Twenty-six parameters were utilised to estimate physical, societal, geotechnical and structural vulnerabilities and integrated earthquake vulnerability map. The results show that more than 50% of the population residing in the region are relatively highly vulnerable due to earthquakes, ~ 25.81% are moderately vulnerable, and ~ 23.44% have low to very low vulnerability. Identifying the most active source zones and estimating vulnerability could be extremely useful for hazard mitigation in the Himalayan region.

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Acknowledgements

The authors thank the United States Geological Survey (USGS) for providing earthquake data. The authors thank the Indian Institute of Technology Kharagpur (ISIRD), and the Ministry of Education, Govt. of India, for providing resources and financial support to conduct this study. The authors sincerely thank the Editor and the anonymous reviewers for their constructive comments which significantly improved the original work.

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  1. Centre for Ocean, River, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India

    Sukanta Malakar & Abhishek K. Rai

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  1. Sukanta Malakar
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Malakar, S., Rai, A.K. Seismicity clusters and vulnerability in the Himalayas by machine learning and integrated MCDM models. Arab J Geosci 15, 1674 (2022). https://doi.org/10.1007/s12517-022-10946-1

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