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A hybrid machine learning and computing model for forecasting displacement of multifactor-induced landslides

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Abstract

A novel hybrid model composed of least squares support vector machines (LSSVM) and double exponential smoothing (DES) was proposed and applied to calculate one-step ahead displacement of multifactor-induced landslides. The wavelet de-noising and Hodrick-Prescott filter methods were used to decompose the original displacement time series into three components: periodic term, trend term and random noise, which respectively represent periodic dynamic behaviour of landslides controlled by the seasonal triggers, the geological conditions and the random measuring noise. LSSVM and DES models were constructed and trained to forecast the periodic component and the trend component, respectively. Models’ inputs include the seasonal triggers (e.g. reservoir level and rainfall data) and displacement values which are measurable variables in a specific prior time. The performance of the hybrid model was evaluated quantitatively. Calculated displacement from the hybrid model is excellently consistent with actual monitored value. Results of this work indicate that the hybrid model is a powerful tool for predicting one-step ahead displacement of landslide triggered by multiple factors.

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Acknowledgements

This study was supported by the National Basic Research Program (973 Program) (grant numbers: 2013CB733200, 2014CB744703), the Funds for Creative Research Groups of China (grant number: 41521002) and the National Natural Science Foundation of China (grant number: 41502293).

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Authors and Affiliations

  1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu, 610059, China

    Xing Zhu, Qiang Xu, Minggao Tang & Huajin Li

  2. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Fangzhou Liu

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  1. Xing Zhu
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  2. Qiang Xu
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  3. Minggao Tang
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  4. Huajin Li
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  5. Fangzhou Liu
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Correspondence to Qiang Xu.

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Zhu, X., Xu, Q., Tang, M. et al. A hybrid machine learning and computing model for forecasting displacement of multifactor-induced landslides. Neural Comput & Applic 30, 3825–3835 (2018). https://doi.org/10.1007/s00521-017-2968-x

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