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Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models

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Abstract

Research on the dynamics of landslide displacement forms the basis for landslide hazard prevention. This paper proposes a novel data-driven approach to monitor and predict the landslide displacement. In the first part, autoregressive moving average time series models are constructed to analyze the autocorrelation of landslide triggering factors. A linear ensemble-based extreme learning machine using the least absolute shrinkage and selection operator is applied in predicting the displacement of landslides. Five benchmarking data-driven models, the support vector machine, neural network, random forest, k-nearest neighbor, and the classical extreme learning machine, are considered as baseline models for validating the ensemble-based extreme learning machines. Numerical experiments demonstrated that the proposed prediction model produces the smallest prediction errors among all the algorithms tested. In the second part, parametric copula models are fitted on the predicted displacement, to investigate the relationship between the triggering factors and landslide displacement values. The Gumbel-Hougaard copula model performs best, which indicates strong upper tail correlation between the triggering factors and displacement values. Thresholds for the triggering factors can be obtained by monitoring the landslide moving patterns with large displacement values. The effectiveness and utility of the proposed data-driven approach have been confirmed with the landslide case study in the region of the Three Gorges Reservoir.

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Acknowledgements

We are immensely grateful to the suggestions and guidance from Prof. T. W. J. van Asch from Utrecht University.

Funding

This research was supported by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 41521002) and the Key Program of National Natural Science Foundation of China (Grant No. 41630640).

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Authors and Affiliations

  1. State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection, Chengdu University of Technology, No. 1 Erxianqiao East Rd., Chengdu, 610059, Sichuan, China

    Huajin Li & Qiang Xu

  2. Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA, 52242, USA

    Yusen He

  3. College of Computing and Digital Media, DePaul University, Chicago, IL, 60604, USA

    Jiahao Deng

Authors
  1. Huajin Li
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  2. Qiang Xu
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  3. Yusen He
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  4. Jiahao Deng
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Corresponding author

Correspondence to Qiang Xu.

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Li, H., Xu, Q., He, Y. et al. Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models. Landslides 15, 2047–2059 (2018). https://doi.org/10.1007/s10346-018-1020-2

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  • DOI: https://doi.org/10.1007/s10346-018-1020-2

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