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Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India

Author

Listed:
  • Somnath Bera

    (Tata Institute of Social Sciences)

  • Vaibhav Kumar Upadhyay

    (Indian Institute of Technology Kanpur)

  • Balamurugan Guru

    (Tata Institute of Social Sciences
    Central University of Tamil Nadu)

  • Thomas Oommen

    (Michigan Technological University)

Abstract

Landslide susceptibility modeling is complex as it involves different types of landslides and diverse interests of the end-user. Developing mitigation strategies for the landslides depends on their typology. Therefore, a landslide susceptibility based on different types should be more appealing than a susceptibility model based on a single inventory set. In this research, susceptibility models are generated considering the different types of landslides. Prior to the development of the model, we analyzed landslide inventory for understanding the complexity and scope of alternative landslide susceptibility mapping. We conducted this work by examining a case study of Kalimpong region (Himalayas), characterized by different types of landslides. The landslide inventory was analyzed based on its differential attributes, such as movement types, state of activity, material type, distribution, style, and failure mechanism. From the landslide inventory, debris slides, rockslides, and rockfalls were identified to generate two landslide susceptibility models using deep learning algorithms. The findings showed high accuracy for both models (above 0.90), although the spatial agreement is highly varied among the models.

Suggested Citation

  • Somnath Bera & Vaibhav Kumar Upadhyay & Balamurugan Guru & Thomas Oommen, 2021. "Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India," 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 1257-1289, August.
    • Handle: RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04731-8
    DOI: 10.1007/s11069-021-04731-8
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    References listed on IDEAS

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    1. Michele Santangelo & Dario Gioia & Mauro Cardinali & Fausto Guzzetti & Marcello Schiattarella, 2015. "Landslide inventory map of the upper Sinni River valley, Southern Italy," Journal of Maps, Taylor & Francis Journals, vol. 11(3), pages 444-453, May.
    2. Raquel Melo & José Luís Zêzere, 2017. "Modeling debris flow initiation and run-out in recently burned areas using data-driven methods," 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. 88(3), pages 1373-1407, September.
    3. Massimo Conforti & Stefania Pascale & Francesco Sdao, 2015. "Mass movements inventory map of the Rubbio stream catchment (Basilicata - South Italy)," Journal of Maps, Taylor & Francis Journals, vol. 11(3), pages 454-463, May.
    4. 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.
    5. K. S. Sajinkumar & S. Rinu & T. Oommen & C. L. Vishnu & K. R. Praveen & V. R. Rani & C. Muraleedharan, 2020. "Improved rainfall threshold for landslides in data sparse and diverse geomorphic milieu: a cluster analysis based approach," 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. 103(1), pages 639-657, August.
    6. Steven Finlay, 2012. "Credit Scoring, Response Modeling, and Insurance Rating," Palgrave Macmillan Books, Palgrave Macmillan, edition 0, number 978-1-137-03169-3.
    7. Jerome Graff & H. Romesburg & Rafi Ahmad & James McCalpin, 2012. "Producing landslide-susceptibility maps for regional planning in data-scarce regions," 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. 64(1), pages 729-749, October.
    8. Christos Polykretis & Christos Chalkias, 2018. "Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network 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. 93(1), pages 249-274, August.
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