A Landslide Deformation Analysis Method Using V/S and LSTM

FENG Feifan; WU Xueling; NIU Ruiqing; XU Shiluo

doi:10.13203/j.whugis20170218

Volume 44 Issue 5

May 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(5): 784-790. > DOI: 10.13203/j.whugis20170218

FENG Feifan, WU Xueling, NIU Ruiqing, XU Shiluo. A Landslide Deformation Analysis Method Using V/S and LSTM[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 784-790. DOI: 10.13203/j.whugis20170218

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A Landslide Deformation Analysis Method Using V/S and LSTM

FENG Feifan^{1, 2, 3,},
WU Xueling^2, ,,
NIU Ruiqing²,
XU Shiluo²

1.
Key Laboratory of Urban Land Resources Monitoring and Simulation, the Ministry of Land and Resources, Shenzhen 518034, China
2.
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
3.
The Fourth Geological Exploration Institute of Henan Geology and Mineral Bureau, Zhengzhou, 450001, China

Funds:

National Natural Science Foundation of China 41871355

National Natural Science Foundation of China 41571438

Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, the Ministry of Land and Resources KF-2015-01-006

More Information

Author Bio:
FENG Feifan, master, specializes in remote sensing geological research. E-mail: giserfan@163.com
Corresponding author:
WU Xueling, PhD, associate professor. E-mail: snowforesting@163.com
Received Date: October 23, 2018
Published Date: May 04, 2019

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Abstract

Abstract

Landslide deformation is the result of the combination of the geological conditions and the external induced factors. The quantitative prediction of landslide deformation is the key to landslide monitoring and early warning. The traditional method based on cumulative displacement-time curve of landslide neglects the influence factors of landslide deformation and evolution, it is difficult to predict landslide deformation accurately. Landslide research in the Three Gorges reservoir area is mostly concentrated on the temporal and spatial distribution characteristics of landslides and the stability analysis of landslides. It is urgent to carry out comprehensive deformation analysis of single landslides. Baishuihe landslide is selected as a case study. Based on landslide macroscopic deformation and displacement monitoring data, spatial-temporal deformation trend of the landslide is analyzed using V/S analysis. Then, long short-term memory neural network model is constructed which consider the influence factors of reservoir water level fluctuation and rainfall hysteresis. It can effectively use the long-term dependent information to realize the quantitative prediction of landslide displacement. The results show that the landslide is characterized by traction landslide, deformation tendency gradually increases from southwest to northeast, and the west and trailing edge are relatively stable. The prediction error is 8.95 mm, which proves the model is of great performance to analyze landslide deformation.
- landslide,
- deformation trend,
- V/S analysis,
- LSTM neural network

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References (20)

References

[1]	Cajueiro D O, Tabak B M.The Rescaled Variance Statistic and the Determination of the Hurst Exponent[J].Mathematics and Computers in Simulation, 2005, 70(3):172-179 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fbbeae1055ecfdd475aa7c3f6fa482ef
[2]	孙东永, 畅建霞, 黄强, 等.V/S和Mann-Kendall相结合的方法在洪涝灾情分析中的应用[J].西北农林科技大学学报(自然科学版), 2012, 40(4):230-234 http://www.cnki.com.cn/Article/CJFDTotal-XBNY201204039.htm Sun Dongyong, Chang Jianxia, Huang Qiang, et al.Application of the Combination of V/S and Mann-Kendall Method in Flood Disaster Analysis[J].Journal of Northwest A and F University (Natural Science Edition), 2012, 40(4):230-234 http://www.cnki.com.cn/Article/CJFDTotal-XBNY201204039.htm
[3]	顾荣宝, 陈霁霞.基于分形V/S技术的沪深股市长记忆性研究[J].安徽大学学报(自然科学版), 2008, 32(3):18-21 doi: 10.3969/j.issn.1000-2162.2008.03.006 Gu Rongbao, Chen Jixia.Long Memory Testing for Shanghai and Shenzhen Stock Markets Based on the V/S Analysis[J].Journal of Anhui University (Natural Sciences), 2008, 32(3):18-21 doi: 10.3969/j.issn.1000-2162.2008.03.006
[4]	乔美英, 陈鑫, 兰建义.基于V/S分析的瓦斯涌出量分形特性研究[J].中国煤炭, 2014(10):104-110 doi: 10.3969/j.issn.1006-530X.2014.10.024 Qiao Meiying, Chen Xin, Lan Jianyi.Study on Fractal Characteristics of Gas Emission Rate Based on V/S Analysis[J].China Coal, 2014(10):104-110 doi: 10.3969/j.issn.1006-530X.2014.10.024
[5]	Liwicki M, Graves A, Bunke H, et al.A Novel Approach to On-line Handwriting Recognition Based on Bidirectional Long Short-Term Memory Networks[C].The 9th International Conference on Document Analysis and Recognition, Curitiba, Brazil, 2007
[6]	Vinyals O, Kaiser L, Koo T, et al.Grammar as a Foreign Language[C].Advances in Neural Information Processing Systems, Montreal, Canada, 2015
[7]	梁军, 柴玉梅, 原慧斌, 等.基于极性转移和LSTM递归网络的情感分析[J].中文信息学报, 2015, 29(5):152-159 doi: 10.3969/j.issn.1003-0077.2015.05.020 Liang Jun, Chai Yumei, Yuan Huibin, et al.Polarity Shifting and LSTM Based Recursive Networks for Sentiment Analysis[J].Journal of Chinese Information Processing, 2015, 29(5):152-159 doi: 10.3969/j.issn.1003-0077.2015.05.020
[8]	杨训政, 柯余洋, 梁肖, 等.基于LSTM的发电机组污染物排放预测研究[J].电气自动化, 2016, 38(5):22-25 doi: 10.3969/j.issn.1000-3886.2016.05.007 Yang Xunzheng, Ke Yuyang, Liang Xiao, et al.A Study on the Prediction of Generator Set Pollutant Emissions Based on LSTM[J].Electrical Automation, 2016, 38(5):22-25 doi: 10.3969/j.issn.1000-3886.2016.05.007
[9]	Giraitis L, Kokoszka P, Leipus R, et al.Rescaled Variance and Related Tests for Long Memory in Volatility and Levels[J].Journal of Econometrics, 2003, 126(2):265-294 doi: 10.1016-S0304-4076(02)00197-5/
[10]	Peters E E.分形市场分析: 将混沌理论应用到投资与经济理论上[M].北京: 经济科学出版社, 2002 Peters E E.Fractal Market Analysis: Appling Chaos Theory to Investment and Economics[M].Beijing: Economic Science Press, 2002
[11]	Peters E E.资本市场的混沌与秩序[M].北京: 经济科学出版社, 1999 Peters E E.Chaos and Order in the Capital Markets[M].Beijing: Economic Science Press, 1999
[12]	Hochreiter S, Schmidhuber J.Long Short-Term Memory[J].Neural Computation, 1997, 9(8):1735-1780 doi: 10.1162/neco.1997.9.8.1735
[13]	Gers F A, Schmidhuber J, Cummins F.Learning to Forget:Continual Prediction with LSTM[J].Neural Computation, 2000, 12(10):2451-2471 doi: 10.1162/089976600300015015
[14]	胡新辰.基于LSTM的语义关系分类研究[D].哈尔滨: 哈尔滨工业大学, 2015 Hu Xinchen.Research on Semantic Relation Classification Based on LSTM[D].Harbin: Harbin Institute of Technology, 2015
[15]	王建锋.滑坡发生时间预报分析[J].中国地质灾害与防治学报, 2003, 14(2):1-8 doi: 10.3969/j.issn.1003-8035.2003.02.001 Wang Jianfeng.Quantitative Prediction of Landslide Using S-curve[J].The Chinese Journal of Geological Hazard and Control, 2003, 14(2):1-8 doi: 10.3969/j.issn.1003-8035.2003.02.001
[16]	张军.滑坡监测分析预报的非线性理论和方法[M].北京:中国水利水电出版社, 2010 Zhang Jun.Landslide Deformation Monitoring Analysis and Prediction Based on Nonliner Theory and Method[M].Beijing:China Water and Power Press, 2010
[17]	李晓, 张年学, 廖秋林, 等.库水位涨落与降雨联合作用下滑坡地下水动力场分析[J].岩石力学与工程学报, 2004, 23(21):3714-3720 doi: 10.3321/j.issn:1000-6915.2004.21.026 Li Xiao, Zhang Nianxue, Liao Qiulin, et al.Analysis on Hydrodynamic Field Influenced by Combination of Rainfall and Reservoir Level Fluctuation[J].Chinese Journal of Rock Mechanics and Engineering, 2004, 23(21):3714-3720 doi: 10.3321/j.issn:1000-6915.2004.21.026
[18]	刘小伟, 刘高, 谌文武, 等.降雨对边坡变形破坏影响的综合分析[J].岩石力学与工程学报, 2003, 22(S2):2715-2718 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb2003z2038 Liu Xiaowei, Liu Gao, Chen Wenwu, et al.Analysis of Rainfall Influence on Slope Deformation and Failure[J].Chinese Journal of Rock Mechanics and Engineering, 2003, 22(S2):2715-2718 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb2003z2038
[19]	Zhang G P.Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model[J].Neurocomputing, 2003, 50(1):159-175 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_f6f039d10ddb9601ced8521cbf8557c1
[20]	Cryer J D, Chan K S.时间序列分析及应用: R语言[M].北京: 机械工业出版社, 2011 Cryer J D, Chan K S.Time Series Analysis with Applications in R[M].Beijing: China Machine Press, 2011

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A Landslide Deformation Analysis Method Using V/S and LSTM

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