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Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors

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Abstract

In the evolution of landslides, besides the geological conditions, displacement depends on the variation of the controlling factors. Due to the periodic fluctuation of the reservoir water level and the precipitation, the shape of cumulative displacement-time curves of the colluvial landslides in the Three Gorges Reservoir follows a step function. The Baijiabao landslide in the Three Gorges region was selected as a case study. By analysing the response relationship between the landslide deformation, the rainfall, the reservoir water level and the groundwater level, an extreme learning machine was proposed in order to establish the landslide displacement prediction model in relation to controlling factors. The result demonstrated that the curves of the predicted and measured values were very similar, with a correlation coefficient of 0.984. They showed a distinctive step-like deformation characteristic, which underlined the role of the influencing factors in the displacement of the landslide. In relation to controlling factors, the proposed extreme learning machine (ELM) model showed a great ability to predict the Baijiabao landslide and is thus an effective displacement prediction method for colluvial landslides with step-like deformation in the Three Gorges Reservoir region.

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Acknowledgments

This paper was prepared with the help of the ‘Research on risk management of intra-county geologic hazards’ project (No. 1212011220173), supported by the China Geological Survey. Thanks are due to the colleagues in our team for their constructive comments and assistance in collecting the data. The authors acknowledge the Institute for Risk and Disaster Reduction of the University College London for the guidance. The first author wishes to thank the China Scholarship Council for funding her research stay at UCL-IRDR.

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Authors and Affiliations

  1. School of Engineering, China University of Geosciences (Wuhan), Wuhan, China

    Ying Cao, Kunlong Yin & Chao Zhou

  2. Institute for Risk and Disaster Reduction, University College London, London, WC1E 6BT, UK

    David E. Alexander

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  1. Ying Cao
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  2. Kunlong Yin
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  3. David E. Alexander
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  4. Chao Zhou
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Correspondence to Kunlong Yin.

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Cao, Y., Yin, K., Alexander, D.E. et al. Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides 13, 725–736 (2016). https://doi.org/10.1007/s10346-015-0596-z

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  • DOI: https://doi.org/10.1007/s10346-015-0596-z

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