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Comparative review of data-driven landslide susceptibility models: case study in the Eastern Andes mountain range of Colombia

Author

Listed:
  • Wilmar Calderón-Guevara

    (Universidad de los Andes
    Universidad de los Andes)

  • Mauricio Sánchez-Silva

    (Universidad de los Andes)

  • Bogdan Nitescu

    (Universidad de los Andes)

  • Daniel F. Villarraga

    (Universidad de los Andes)

Abstract

Estimating the likelihood of landslides has proven to be critical for development and protection of infrastructure (e.g. pipelines, roads) and urban settlements. Currently, for regional studies of landslide susceptibility only qualitative or statistical evaluations are possible due to the large spatial variability of geological properties, topography, rainfall patterns, etc. In this paper, we explore an alternative to these approaches using data-driven methodologies to determine landslide susceptibility. We give special attention to the use of geographical information systems, machine learning and statistical techniques to build landslide susceptibility maps. These methods have input as fourteen key causative factors that might influence landslides occurrence. Additionally, feature extraction and feature selection are performed to evaluate if dimensionality reduction increases the prediction accuracy of the machine learning models. The models were compared using a case study in the Eastern Cordillera of Colombia, where the best performing model achieved a predictive performance of $$93.07\%$$ 93.07 % .

Suggested Citation

  • Wilmar Calderón-Guevara & Mauricio Sánchez-Silva & Bogdan Nitescu & Daniel F. Villarraga, 2022. "Comparative review of data-driven landslide susceptibility models: case study in the Eastern Andes mountain range of Colombia," 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. 113(2), pages 1105-1132, September.
    • Handle: RePEc:spr:nathaz:v:113:y:2022:i:2:d:10.1007_s11069-022-05339-2
    DOI: 10.1007/s11069-022-05339-2
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    References listed on IDEAS

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    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
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    Cited by:

    1. G. S. Pradeep & M. V. Ninu Krishnan & H. Vijith, 2023. "Characterising landslide susceptibility of an environmentally fragile region of the Western Ghats in Idukki district, Kerala, India, through statistical modelling and hotspot analysis," 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. 115(2), pages 1623-1653, January.

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