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Spatiotemporal modelling of rainfall-induced landslides using machine learning

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Abstract

Natural terrain landslides are mainly triggered by rainstorms in Hong Kong, which pose great threats to life and property. To mitigate landslide risk, building a prediction model which could provide information on both spatial and temporal probabilities of landslide occurrence is essential but challenging. In this paper, real-time rainfall conditions are incorporated into landslide prediction through a unique rainstorm-based database of reported landslides. Other landslide controlling factors related to topography, geology, and land cover are also considered. Five machine learning methods, including logistic regression, random forest, adaboost tree, support vector machine, and multilayer perceptron, are utilized and compared. Validated against historical rainstorms, the machine learning powered landslide prediction model could reasonably forecast the occurrence of landslides in a spatiotemporal context. Moreover, the effects of different rainstorm characteristics in terms of distinct rainfall spatial distribution and intensity on landslide susceptibility could also be captured by this model. For the landslide controlling factors investigated, rolling rainfall factors are proven to play a more important role than antecedent rainfall factors for landslide prediction. Among the five machine learning methods, the random forest model yields the most promising results in terms of all performance indicators (i.e., classification accuracy, recall, precision, area under curve, and overall accuracy).

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Acknowledgements

The authors would like to acknowledge the Geotechnical Engineering Office of Civil Engineering and Development Department of HKSAR for providing the landslide inventory data.

Funding

The Research Grants Council (RGC) of the Hong Kong Special Administrative Region (HKSAR) provided the research grant (Project No. AoE/E-603/18). The second author received the support of the Hong Kong Ph.D. Fellowship Scheme (HKPFS) provided by the RGC of the HKSAR.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, the Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    C. W. W. Ng & B. Yang

  2. Norwegian Geotechnical Institute, Oslo, Norway

    Z. Q. Liu

  3. Geotechnical Engineering Office, Civil Engineering and Development Department, Kowloon, Hong Kong

    J. S. H. Kwan

  4. Department of Computer Science and Engineering, the Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    L. Chen

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  1. C. W. W. Ng
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  2. B. Yang
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  4. J. S. H. Kwan
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  5. L. Chen
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Correspondence to B. Yang.

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Ng, C.W.W., Yang, B., Liu, Z.Q. et al. Spatiotemporal modelling of rainfall-induced landslides using machine learning. Landslides 18, 2499–2514 (2021). https://doi.org/10.1007/s10346-021-01662-0

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  • DOI: https://doi.org/10.1007/s10346-021-01662-0

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