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Improved rainfall threshold for landslides in data sparse and diverse geomorphic milieu: a cluster analysis based approach

Author

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  • K. S. Sajinkumar

    (University of Kerala
    Michigan Technological University)

  • S. Rinu

    (University of Texas at Arlington)

  • T. Oommen

    (University of Kerala
    Michigan Technological University)

  • C. L. Vishnu

    (University of Kerala
    Michigan Technological University)

  • K. R. Praveen

    (Geological Survey of India)

  • V. R. Rani

    (Central Ground Water Board)

  • C. Muraleedharan

    (Geological Survey of India)

Abstract

Rainfall-triggered landslides are the most common type of mass movement seen along the tropical belt due to the prevalence of monsoons. These landslides can be forecasted by understanding the spatial and temporal rainfall distribution patterns, and subsequent generation of rainfall threshold (RT). However, deriving a regional RT in a geologically, geographically and physiographically diverse milieu is a formidable task. The data on spatial and intra-seasonal variability of monsoons can be widely dispersed in such diversified terrains. Clustering analysis provides a promising approach to handle such widely dispersed data. This study intends to develop a methodology using 2-stage clustering process to create RT in such terrains by using daily rainfall versus antecedent rainfall and rainfall versus antecedent rainfall versus soil depth. Sixteen rainfall-induced landslides, located in different terrains in the Western Ghats of India, were subjected to this analysis. Majority of the landslides were modeled, and different RTs were derived for different conditions. The landslides belong to four different classes, viz., landslides occurring at steep slopes; those occurring at knickpoints of highland and midland; in the plateau region and others characterized by a thin veneer of soil. Out of 16 landslides subjected to RT, this method was able to model 13 landslides with a success rate of 81.25%, which is a fair figure.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:103:y:2020:i:1:d:10.1007_s11069-020-04004-w
    DOI: 10.1007/s11069-020-04004-w
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    References listed on IDEAS

    as
    1. K. Sajinkumar & R. Castedo & P. Sundarajan & V. Rani, 2015. "Study of a partially failed landslide and delineation of piping phenomena by vertical electrical sounding (VES) in the Wayanad Plateau, Kerala, 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. 75(1), pages 755-778, January.
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    Cited by:

    1. 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.
    2. Dhanya Madhu & G. K. Nithya & S. Sreekala & Maneesha Vinodini Ramesh, 2024. "Regional-scale landslide modeling using machine learning and GIS: a case study for Idukki district, Kerala, 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. 120(11), pages 9935-9956, September.
    3. Fabio Luino & Jerome De Graff & Marcella Biddoccu & Francesco Faccini & Michele Freppaz & Anna Roccati & Fabrizio Ungaro & Michele D’Amico & Laura Turconi, 2022. "The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)," Land, MDPI, vol. 11(8), pages 1-26, July.
    4. Adrián G. Bruzón & Patricia Arrogante-Funes & Fátima Arrogante-Funes & Fidel Martín-González & Carlos J. Novillo & Rubén R. Fernández & René Vázquez-Jiménez & Antonio Alarcón-Paredes & Gustavo A. Alon, 2021. "Landslide Susceptibility Assessment Using an AutoML Framework," IJERPH, MDPI, vol. 18(20), pages 1-20, October.

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