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“Satark”: Landslide Prediction System over Western Ghats of India

Author

Listed:
  • Jeevan R. Kulkarni

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Sneha S. Kulkarni

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Mitali U. Inamdar

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Nitin M. Tamhankar

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Spandan B. Waghmare

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Kiran R. Thombare

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Paresh S. Mhetre

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Tanuja Khatavkar

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Yashodhan Panse

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Amey Patwardhan

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Yogini Soman

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Prasad Bhagat

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Sumit Bhale

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Mayuresh G. Prabhune

    (Center for Citizen Science, Pankaj Park, Pune 411033, India)

  • Vinay Kumar

    (Department of Physics and Geophysics, Texas A&M University, Kingsville, TX 78363, USA)

Abstract

Mountains on the west coast of India are known as the Western Ghat (WG). The WG region has a landslide (LS) susceptibility index of four and is prone to LSs in the monsoon season due to rainfall activity. The LS study focuses on the area between 15.5–20.5° N, 72.5–77.0° E in the Maharashtra state. A catalog of 115 LS events in the study area has been prepared by collecting LS data for 17 years (2000–2016) from various sources. The area from the windward to the leeward side of the WG mountains is divided into three regions: (1) the windward region (72.5–73.4° E) (90 km width), (2) the immediate lee side (ILS) (73.40–74.20° E) (80 km width), and (3) distant lee side (DLS) (74.2–77.0° E) (280 km width). The Center for Citizen Science (CCS), Pune, India, developed the LS-predicting model “Satark” using data from satellites, the India Meteorological Department weather forecasts, radar products, synoptic conditions, and atmospheric sounding data from the Wyoming site for inferring conditions for a hydraulic jump on the WG. The model validation for the 5 years (2017–2021) showed a reasonably good Heidke skill score of 0.44. The model showed 76.5% success in LS prediction 1 day in advance. It is the first attempt of this kind in the Indian region.

Suggested Citation

  • Jeevan R. Kulkarni & Sneha S. Kulkarni & Mitali U. Inamdar & Nitin M. Tamhankar & Spandan B. Waghmare & Kiran R. Thombare & Paresh S. Mhetre & Tanuja Khatavkar & Yashodhan Panse & Amey Patwardhan & Yo, 2022. "“Satark”: Landslide Prediction System over Western Ghats of India," Land, MDPI, vol. 11(5), pages 1-23, May.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:689-:d:808884
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    References listed on IDEAS

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    1. T. N. Singh & Rajbal Singh & Bhoop Singh & L. K. Sharma & Rajesh Singh & M. K. Ansari, 2016. "Investigations and stability analyses of Malin village landslide of Pune district, Maharashtra, 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. 81(3), pages 2019-2030, April.
    2. Yang Hong & Robert Adler & George Huffman, 2007. "Use of satellite remote sensing data in the mapping of global landslide susceptibility," 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. 43(2), pages 245-256, November.
    3. Dalia Kirschbaum & Robert Adler & Yang Hong & Stephanie Hill & Arthur Lerner-Lam, 2010. "A global landslide catalog for hazard applications: method, results, and limitations," 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. 52(3), pages 561-575, March.
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    1. Pradeep Kumar Badapalli & Anusha Boya Nakkala & Raghu Babu Kottala & Sakram Gugulothu & Fahdah Falah Ben Hasher & Varun Narayan Mishra & Mohamed Zhran, 2025. "Landslide Susceptibility Level Mapping in Kozhikode, Kerala, Using Machine Learning-Based Random Forest, Remote Sensing, and GIS Techniques," Land, MDPI, vol. 14(7), pages 1-25, July.

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