基于时间序列与长短时记忆网络的滑坡位移动态预测模型

doi:10.13722/j.cnki.jrme.2018.0468

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摘要针对滑坡演化的动态特性和传统静态预测模型的不足，提出一种基于时间序列与长短时记忆网络(long and short term memory neural network，LSTM)的滑坡位移动态预测模型。该模型首先采用移动平均法将滑坡累积位移分解为趋势项位移和周期项位移。然后采用多项式函数预测趋势项位移；基于滑坡变形特征与诱发因素的响应分析，建立LSTM模型进行周期项位移预测。最后将各分项位移叠加，即实现滑坡累积位移的预测。以三峡库区典型阶跃型滑坡——白水河滑坡为例，并与支持向量机模型(support vector machine，SVM)进行对比分析。结果表明，与静态模型SVM相比，动态模型LSTM的预测精度较高，在阶跃式变形期的预测优势尤为突出，且不依赖于训练数据时效性的分析。该模型为三峡库区阶跃型滑坡位移预测提供了新的思路和探索。

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	杨背背1
	殷坤龙1
	杜娟2

关键词 ：边坡工程, 阶跃型滑坡, 位移预测, 动态模型, 时间序列, 长短时记忆网络

Abstract：To address the transient characteristics of landslide processes and to overcome the deficiency of static forecasting models，a model for predicting the transient landslide displacement was proposed based on time series theory and long and short term memory neural network(LSTM). In the model，the moving average method was applied to decompose the cumulative displacement into the trend term and periodic term. Subsequently，the trend displacement was predicted by a polynomial model. A LSTM model，based on the response of inducing factors，was established to predict the periodic displacement. Finally，the trend displacement and periodic displacement were superposed to achieve the cumulative displacement. Baishuihe landslide，a typical stepped landslide in Three Gorges Reservoir area，was taken as an example to test the prediction performance of the proposed model and the support vector machine(SVM) was used for comparison. The results demonstrate that the transient model(LSTM) achieves higher prediction accuracy than the static model(SVM)，especially during the period of stepped deformation. Furthermore，the prediction accuracy of the LSTM model is not limited by the timeliness analysis of training sets.

Key words： slope engineering step-like landslide displacement prediction dynamic model time series long and short term memory neural network

引用本文:

杨背背1，殷坤龙1，杜娟2. 基于时间序列与长短时记忆网络的滑坡位移动态预测模型[J]. 岩石力学与工程学报, 2018, 37(10): 2334-2343.
YANG Beibei1，YIN Kunlong1，DU Juan2. A model for predicting landslide displacement based on time series and long and short term memory neural network. , 2018, 37(10): 2334-2343.

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http://www.rockmech.org/CN/10.13722/j.cnki.jrme.2018.0468 或 http://www.rockmech.org/CN/Y2018/V37/I10/2334

[27]	汪洋，殷坤龙，安关峰. 滑坡敏感因子的灰色关联分析[J]. 岩土力学，2004，25(1)：91-93.(WANG Yang，YIN Kunlong，AN Guanfeng. Grey correlation analysis of sensitive factors of landslide[J]. Rock and Soil Mechanics，2004，25(1)：91-93.(in Chinese))
[22]	KEEFER D，WILSON R，ROBERT K，et al. Real-time landslide warning during heavy rainfall[J]. Science，1987，238：921-925.
[2]	邓冬梅，梁烨，王亮清，等. 基于集合经验模态分解与支持向量机回归的位移预测方法：以三峡库区滑坡为例[J]. 岩土力学，2017，38(12)：3 660-3 669.(DENG Dongmei，LIANG Ye，WANG Liangqiang，et al. Displacement prediction method based on ensemble empirical mode decomposition and support vector machine regression—a case of landslides in Three Gorges Reservoir area[J]. Rock and Soil Mechanics，2017，38(12)：3 660-3 669.(in Chinese))
[5]	杜娟，殷坤龙，柴波. 基于诱发因素响应分析的滑坡位移预测模型研究[J]. 岩石力学与工程学报，2009，28(9)：1 783-1 789.(DU Juan，YIN Kunlong，CHAI Bo. Study of displacement prediction model of landslide based on response analysis of inducing factors[J]. Chinese Journal of Rock Mechanics and Engineering，2009，28(9)： 1 783-1 789.(in Chinese))
[8]	姚为，廉城. 基于储备池运算和分形插值的滑坡位移预测[J]. 长江科学院院报，2014，31(12)：43-48.(YAO Wei，LIAN Cheng. Prediction of landslide displacement based on reservoir computing and fractal interpolation[J]. Journal of Yangtze River Scientific Research Institute，2014，31(12)：43-48.(in Chinese))
[6]	DU J，YIN K，LACASSE S. Displacement prediction in colluvial landslides，Three Gorges Reservoir，China[J]. Landslides，2013，10：203-218.
[16]	HOCHREITE S，SCHMIDHUBER J. Long short-term memory[J]. Neural Computation，1997，9(8)：1 735-1 780.
[26]	GLADE T，ANDERSON M，CROZIER M. Landslide hazard and risk[M]. Vienna，Austria：Wiley，2012：1-40.
[1]	许强，黄润秋，李秀珍. 滑坡时间预测预报研究进展[J]. 地球科学进展，2004，19(3)：478-483.(XU Qiang，HUANG Runqiu，LI Xiuzhen. Research progress on the landslide time prediction[J]. Advance in Earth Sciences，2004，19(3)：478-483.(in Chinese))
[9]	YAO W，ZENG Z，LIAN C，et al. Training enhanced reservoir computing predictor for landslide displacement[J]. Engineering Geology，2015，188：101-109.
[3]	张俊，殷坤龙，王佳佳，等. 基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究[J]. 岩石力学与工程学报，2015，34(2)：382-391.(ZHANG Jun，YIN Kunlong，WANG Jiajia，et al. Displacement prediction of Baishuihe landslide based on time series and PSO-SVR model[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(2)：382-391.(in Chinese))
[4]	ZHOU C，YIN K，CAO Y，et al. Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir[J]. Engineering Geology，2016，204：108-120.
[10]	PALANGI H，DENG L，SHEN Y，et al. Deep sentence embedding using the long short-term memory network：analysis and application to information retrieval[J]. IEEE/ACM Transactions on Audio，Speech and Language Processing，2016，24(4)：694-707.
[14]	VINCENT P，LAROCHELLE H，LAJOIE I，et al. Stacked denoising autoencoders：learning useful representation in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research，2010，11(12)：3 371-3 408.
[24]	刘勇，秦志萌，刘曼，等. 基于状态划分的滑坡位移预测方法研究[J]. 地质科学情报，2018，37(1)：184-189.(LIU Yong，QIN Zhimeng，LIU Man，et al. Landslide displacement prediction method based on state division[J]. Geological Science and Technology Information，2018，37(1)：184-189.(in Chinese))
[7]	姚为，廉城，程立. 滑坡位移的动态概率预测模型[J]. 水文地质工程地质，2015，42(5)：134-139+148.(YAO Wei，LIAN Cheng，CHENG Li. A dynamic model for landslide displacement prediction[J]. Hydrogeology and Engineering Geology，2015，42(5)：134-139+148.(in Chinese))
[11]	XU S，NIU R. Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area，China[J]. Computers and Geosciences，2018，111：87-96.
[15]	BENGIO Y，SIMARD P，FRASCONI P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks，1994，5(2)：157-166.
[17]	徐聪. 基于卷积长短时记忆神经网络的时序信号多粒度分析处理方法研究[硕士学位论文][D]. 哈尔滨：哈尔滨工业大学，2017.(XU Cong. Research on multi-granularity analysis method of time sequence signalizing convolution long short term memory network[M. S. Thesis][D]. Harbin：Harbin Institute of Technology，2017.(in Chinese))
[19]	许强，汤明高，徐开祥，等. 滑坡时空演化规律及预警预报研究[J]. 岩石力学与工程学报，2008，27(6)：1 104-1 112.(XU Qiang，TANG Minggao，XU Kaixiang，et al. Research on space-time evolution laws and early warning-prediction of landslides[J]. Chinese Journal of Rock Mechanics and Engineering，2008，27(6)：1 104-1 112.(in Chinese))
[21]	YANG B，YIN K，XIAO T，et al. Annual variation of landslide stability under the effect of water level fluctuation and rainfall in the Three Gorges Reservoir，China[J]. Environmental Earth Sciences，2017，76：564-580.
[25]	龙辉，秦四清，万志清. 降雨触发滑坡的尖点突变模型[J]. 岩石力学与工程学报，2002，21(4)：502-508.(LONG Hui，QIN Siqing，Wan Zhiqing. Catastrophe analysis of rainfall-induced landslides[J]. Chinese Journal of Rock Mechanics and Engineering，2002，21(4)：502-508.(in Chinese))
[12]	徐峰，汪洋，杜娟，等. 基于时间序列分析的滑坡位移预测模型研究[J]. 岩石力学与工程学报，2011，30(4)：746-751.(XU Feng，WANG Yang，DU Juan，et al. Study of displacement prediction model of landslide based on time series analysis[J]. Chinese Journal of Rock Mechanics and Engineering，2011，30(4)：746-751.(in Chinese))
[13]	GRAVES A，MOHAMED A，HINTON G. Speech recognition with deep recurrent neural networks[C]// Proceedings of International Conference on Acoustics，Speech and Signal Processing Acoustics. Vancouver，Canada：IEEE，2013：6 645-6 649.
[18]	CAO Y，YIN K，ALEXANDER D，et al. Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors[J]. Landslides，2016，13：725-736.
[20]	MIAO F，WU Y，XIE Y，et al. Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model[J]. Landslides，2018，15：475-488.
[23]	刘磊. 三峡水库万州区库岸滑坡灾害风险评价研究[博士学位论文][D]. 武汉：中国地质大学，2016.(LIU Lei. Assessment of landslide risk along Wanzhou area in Three Gorges Reservoir[Ph. D. Thesis][D]. Wuhan：China University of Geosciences，2016.(in Chinese))
[28]	李麟玮，吴益平，苗发盛，等. 基于变分模态分解与GWO-MIC-SVR模型的滑坡位移预测研究[J]. 岩石力学与工程学报，2018，37(6)： 1 395-1 406.(LI Linwei，WU Yiping，MIAO Fasheng，et al. Displacement prediction of landslide based on variational mode decomposition and GWO-MIC-SVR model[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(6)：1 395-1 406.(in Chinese))