Full Text:   <2394>

Summary:  <1621>

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: 2903

Citations:  Bibtex RefMan EndNote GB/T7714


Zhong-qiang Liu


-   Go to

Article info.
Open peer comments

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


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.

@article{title="Algorithms for intelligent prediction of landslide displacements",
author="Zhong-qiang Liu, Dong Guo, Suzanne Lacasse, Jin-hui Li, Bei-bei Yang, Jung-chan Choi",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Algorithms for intelligent prediction of landslide displacements
%A Zhong-qiang Liu
%A Dong Guo
%A Suzanne Lacasse
%A Jin-hui Li
%A Bei-bei Yang
%A Jung-chan Choi
%J Journal of Zhejiang University SCIENCE A
%V 21
%N 6
%P 412-429
%@ 1673-565X
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000005

T1 - Algorithms for intelligent prediction of landslide displacements
A1 - Zhong-qiang Liu
A1 - Dong Guo
A1 - Suzanne Lacasse
A1 - Jin-hui Li
A1 - Bei-bei Yang
A1 - Jung-chan Choi
J0 - Journal of Zhejiang University Science A
VL - 21
IS - 6
SP - 412
EP - 429
%@ 1673-565X
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2000005

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.


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


[1]Bai SB, Wang J, Lü GN, et al., 2010. GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology, 115(1-2):23-31.

[2]Breiman L, 2001. Random forests. Machine Learning, 45(1):5-32.

[3]Cao Y, Yin KL, Alexander DE, et al., 2016. Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides, 13(4):725-736.

[4]Chen SY, Chou WY, 2012. Short-term traffic flow prediction using EMD-based recurrent hermite neural network approach. Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems, p.1821-1826.

[5]CIGM (China Institute of Geo-environment Monitoring), 2017. Bulletin of geologic hazards from January to December in 2016. China Institute of Geo-environment Monitoring, Beijing, China (in Chinese).

[6]Cho K, van Merriënboer B, Gulcehre C, et al., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, p.1724-1734.

[7]Corominas J, Moya J, Ledesma A, et al., 2005. Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain). Landslides, 2(2):83-96.

[8]Crozier MJ, Glade T, 2005. Landslide hazard and risk: issues, concepts and approach. In: Glade T, Anderson M, Crozier MJ (Eds.), Landslide Hazard and Risk. John Wiley and Sons Ltd., p.1-40.

[9]Du J, Yin KL, Lacasse S, 2013. Displacement prediction in colluvial landslides, Three Gorges reservoir, China. Landslides, 10(2):203-218.

[10]Han M, Xi JH, Xu SG, et al., 2004. Prediction of chaotic time series based on the recurrent predictor neural network. IEEE Transactions on Signal Processing, 52(12):3409-3416.

[11]Huang FM, Yin KL, Zhang GR, et al., 2016. Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory. Environmental Earth Sciences, 75(20):1376.

[12]Intrieri E, Raspini F, Fumagalli E, et al., 2018. The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data. Landslides, 15:123-133.

[13]Li JH, Li PX, Guo D, et al., 2020. Advanced prediction of tunnel boring machine performance based on big data. Geoscience Frontiers, in press.

[14]Liu ZB, Shao JF, Xu WY, et al., 2014. Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches. Landslides, 11(5):889-896.

[15]Liu ZQ, Gilbert G, Cepeda JM, et al., 2020. Modelling of shallow landslides with machine learning algorithms. Geoscience Frontiers, in press.

[16]Ma JW, Tang HM, Liu X, et al., 2017. Establishment of a deformation forecasting model for a step-like landslide based on decision tree C5.0 and two-step cluster algorithms: a case study in the Three Gorges Reservoir area, China. Landslides, 14(3):1275-1281.

[17]Ma JW, Tang HM, Liu X, et al., 2018. Probabilistic forecasting of landslide displacement accounting for epistemic uncertainty: a case study in the Three Gorges Reservoir area, China. Landslides, 15(6):1145-1153.

[18]McDougall S, 2017. 2014 Canadian geotechnical colloquium: landslide runout analysis–current practice and challenges. Canadian Geotechnical Journal, 54(5):605-620.

[19]Miao FS, Wu YP, Xie YH, et al., 2018. Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model. Landslides, 15(3):475-488.

[20]Pham BT, Prakash I, Tien Bui D, 2018. Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees. Geomorphology, 303:256-270.

[21]Qin SQ, Jiao JJ, Wang SJ, 2002. A nonlinear dynamical model of landslide evolution. Geomorphology, 43(1-2):77-85.

[22]Quinlan JR, 1993. C4.5 Programs for Machine Learning. Morgan Kaufmann Publishers, California, USA, p.17-45.

[23]Ran QH, Su DY, Qian Q, et al., 2012. Physically-based approach to analyze rainfall-triggered landslide using hydraulic gradient as slide direction. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 13(12):943-957.

[24]Selby MJ, 1988. Landslides: causes, consequences and environment. Journal of the Royal Society of New Zealand, 18(3):343.

[25]Tien Bui D, Tuan TA, Klempe H, et al., 2016. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13:361-378.

[26]Vincent P, Larochelle H, Lajoie I, et al., 2010. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research, 11(12):3371-3408.

[27]Xu SL, Niu RQ, 2018. Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China. Computers & Geosciences, 111:87-96.

[28]Yang BB, Yin KL, Xiao T, et al., 2017. Annual variation of landslide stability under the effect of water level fluctuation and rainfall in the Three Gorges Reservoir, China. Environmental Earth Sciences, 76(16):564.

[29]Yang BB, Lacasse S, Yin KL, et al., 2018. Factors influencing landslide deformation from observations in the Three Gorges Reservoir. In: Wu W, Yu HS (Eds.), Proceedings of China-Europe Conference on Geotechnical Engineering. Springer, Switzerland, p.1551-1555.

[30]Yang BB, Yin KL, Liu ZQ, et al., 2019a. Machine learning to predict landslide displacement in dam reservoir. Proceedings of the ICOLD Symposium, p.1-13.

[31]Yang BB, Yin KL, Lacasse S, et al., 2019b. Time series analysis and long short-term memory neural network to predict landslide displacement. Landslides, 16(4):677-694.

[32]Zhang P, Yin ZY, Jin YF, et al., 2020. A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest. Engineering Geology, 265:105328.

[33]Zhou C, Yin KL, Cao Y, et al., 2016. Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China. Engineering Geology, 204:108-120.

[34]Zhou C, Yin KL, Cao Y, et al., 2018a. Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method. Landslides, 15(11):2211-2225.

[35]Zhou C, Yin KL, Cao Y, et al., 2018b. Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Computers & Geosciences, 112:23-27.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE