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CLC number: P642

On-line Access: 2020-06-10

Received: 2020-01-11

Revision Accepted: 2020-05-15

Crosschecked: 2020-05-23

Cited: 0

Clicked: 1530

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhong-qiang Liu

https://orcid.org/0000-0002-1693-5746

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Journal of Zhejiang University SCIENCE A 2020 Vol.21 No.6 P.412-429

http://doi.org/10.1631/jzus.A2000005


Algorithms for intelligent prediction of landslide displacements


Author(s):  Zhong-qiang Liu, Dong Guo, Suzanne Lacasse, Jin-hui Li, Bei-bei Yang, Jung-chan Choi

Affiliation(s):  Norwegian Geotechnical Institute (NGI), Oslo 0855, Norway; more

Corresponding email(s):   suzanne.lacasse@ngi.no, jinhui.li@hit.edu.cn

Key Words:  Landslide, Displacement, Machine learning, Three Gorges Dam reservoir


Zhong-qiang Liu, Dong Guo, Suzanne Lacasse, Jin-hui Li, Bei-bei Yang, Jung-chan Choi. Algorithms for intelligent prediction of landslide displacements[J]. Journal of Zhejiang University Science A, 2020, 21(6): 412-429.

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author="Zhong-qiang Liu, Dong Guo, Suzanne Lacasse, Jin-hui Li, Bei-bei Yang, Jung-chan Choi",
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T1 - Algorithms for intelligent prediction of landslide displacements
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A1 - Dong Guo
A1 - Suzanne Lacasse
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A1 - Bei-bei Yang
A1 - Jung-chan Choi
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Abstract: 
landslides represent major threats to life and property in many areas of the world, such as the landslides in the Three Gorges Dam area in mainland China. To better prepare for landslides in this area, we explored how several machine learning algorithms (long short term memory (LSTM), random forest (RF), and gated recurrent unit (GRU)) might predict ground displacements under three types of landslides, each with distinct step-wise displacement characteristics. landslide displacements are described with trend and periodic analyses and the predictions with each algorithm, validated with observations from the three Gorges Dam reservoir over a one-year period. Results demonstrated that deep machine learning algorithms can be valuable tools for predicting landslide displacements, with the LSTM and GRU algorithms providing the most encouraging results. We recommend using these algorithms to predict landslide displacement of step-wise type landslides in the Three Gorges Dam area. Predictive models with similar reliability should gradually become a component when implementing early warning systems to reduce landslide risk.

边坡位移智能预测算法

目的:边坡位移预测是实现滑坡灾害预报的有效手段,对降低滑坡灾害导致的损失具有重要意义. 本文针对三峡库区广泛分布的"阶跃型"滑坡,采用三种不同的机器学习算法:长短期记忆(LSTM)神经网络、随机森林(RF)算法和门控递归单元(GRU),预测三个不同的三峡库区边坡位移,并对比三种算法的预测精度,从而选择适用于边坡位移预测的机器学习算法.
创新点:1. 建立了基于时间序列分解和机器学习算法的动态预测模型,并能够准确预测边坡位移. 2. 对比了不同的机器学习算法预测边坡周期项位移的精度.
方法:1. 基于时间序列分解原理,将边坡累积位移分解为趋势项位移和周期项位移. 2. 利用多项式拟合对边坡趋势项位移进行预测. 3. 基于位移影响因素采用三种机器学习模型(LSTM、GRU和RF)预测边坡周期项位移.
结论:1. 本文提出的基于时间序列分解和机器学习算法的动态预测模型可以准确预测三峡库区"阶跃型"边坡位移. 2. LSTM和GRU算法可以充分利用滑坡历史信息,精确预测边坡位移的周期项.

关键词:滑坡; 位移; 机器学习; 三峡库区

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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