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Regional-scale landslide modeling using machine learning and GIS: a case study for Idukki district, Kerala, India

Author

Listed:
  • Dhanya Madhu

    (Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham
    Amrita Vishwa Vidyapeetham)

  • G. K. Nithya

    (Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham)

  • S. Sreekala

    (Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham)

  • Maneesha Vinodini Ramesh

    (Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham)

Abstract

Globally, landslides impact in a site-specific and regional scale, and have affected 4.8 million human beings during1998–2017. Landslides, being a highly complex phenomenon involving real-time and near real-time interactions between hydrological, geomorphological, climatological as well as anthropological factors impacting large spatial areas, demand the development of regional-scale warning systems. Even though an extensive body of research already exists in the field of landslide early warning, the prediction of the actual location and the time of landslide initiation is still a major challenge. In the current study, we compare the performance of ten machine learning (ML) algorithms useful for landslide early warning. The current study is performed in the Idukki district of Kerala state in India. A database with landslide incidents is created using research literature, reports from the Geological Survey of India (GSI) as well as news articles. Landslide causative factors, as indicated by previous literature, have been mapped using Geographic Information System (GIS). The different ML algorithms considered for this study are Decision Tree, Logistic Regression, K Nearest Neighbor, Gaussian Naive Bayes, Support Vector Machine, and its different kernel functions such as linear, Polynomial, Gaussian and ensemble algorithms namely Random Forest and AdaBoost. The performance of the different algorithms is quantified and compared utilizing established statistical metrics such as G-Mean, F1 score, and ROC-AUC score. Our results follow the similar ones in literature where the machine learning techniques provide an efficient tool for landslide susceptibility mapping. All the algorithms considered produce reasonable results.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:nathaz:v:120:y:2024:i:11:d:10.1007_s11069-024-06592-3
    DOI: 10.1007/s11069-024-06592-3
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    References listed on IDEAS

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    1. Maher Maalouf, 2011. "Logistic regression in data analysis: an overview," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(3), pages 281-299.
    2. Dieu Tien Bui & Biswajeet Pradhan & Owe Lofman & Inge Revhaug & Øystein Dick, 2013. "Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam," 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. 66(2), pages 707-730, March.
    3. Faraz S. Tehrani & Michele Calvello & Zhongqiang Liu & Limin Zhang & Suzanne Lacasse, 2022. "Machine learning and landslide studies: recent advances and applications," 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. 114(2), pages 1197-1245, November.
    4. 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.
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    More about this item

    Keywords

    Landslide; Machine learning; Geoscience data; F1 score; G-mean; AUC score;
    All these keywords.

    JEL classification:

    • F1 - International Economics - - Trade

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