基于变分模态分解与GWO-MIC-SVR模型的滑坡位移预测研究

doi:10.13722/j.cnki.jrme.2017.1508

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (649 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对目前滑坡位移预测研究中存在的随机性位移无法分解与预测、支持向量机(SVR)模型中输入向量权值无差异、最优训练组合无法确定等问题，基于时序分析理论和变分模态分解(VMD)，结合灰狼优化算法(GWO)、最大信息系数(MIC)和SVR，提出一种新型滑坡位移预测模型。该模型首先采用时序分析法和VMD，将滑坡实际累积位移分解为趋势性位移、周期性位移和随机性位移。然后基于滑坡对各类影响因素的响应分析，为3种位移选择合适的影响因子，并采用GWO-MIC-SVR模型对各位移分量进行多数据驱动的动态单步预测。最终基于训练数据的时效性分析，确定最优训练组合，并应用时序加法模型将最优预测值进行叠加，实现对滑坡累积位移的预测。以三峡库区典型堆积层滑坡——白水河滑坡为例，选取监测点ZG93和ZG118从2004年1月～2013年4月的数据进行研究。结果表明，与以往的研究相比，该模型不仅有效预测时间较长，且预测精度较高，具有较高的实用性和推广应用价值。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	李麟玮
	吴益平
	苗发盛
	廖康
	张龙飞

关键词 ：边坡工程, 滑坡位移预测, 变分模态分解, 灰狼优化, 最大信息系数, 支持向量回归

Abstract：This paper focused on some issues relating to the decomposition and prediction of stochastic displacement，the weight calculation of input vectors in support vector regression(SVR) and the determination of optimal training combination. A modified model of displacement prediction integrated with the gray wolf optimizer(GWO)，the maximum information coefficient(MIC) and the SVR，was proposed based on the time series theory and the variational mode decomposition(VMD). In this model，the time series analysis and the VMD were firstly applied to decompose the cumulative landslide displacement into the trend displacement，the periodic displacement and the stochastic displacement. Subsequently，some reasonable inducing factors were selected according to the response analysis of landslide，and then the single step prediction supported by multiple data was implemented by using the GWO-MIC-SVR model. Finally，the optimal training combination was confirmed based on the timeliness analysis of training sets，and the optimal values were superposed to achieve the prediction of cumulative displacement. Baishuihe Landslide，a typical colluvial landslide in the area of Three Gorges Reservoir，was taken as an example. The monitoring data of ZG93 and ZG118 from January 2004 to April 2013 were analyzed. The results show that compared with previous studies，this model has longer period of effective prediction and higher accuracy in prediction.

Key words： slope engineering landslide displacement prediction variational mode decomposition grey wolf optimizer maximum information coefficient support vector regression

引用本文:

李麟玮，吴益平，苗发盛，廖康，张龙飞. 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): 1395-1406.
LI Linwei，WU Yiping，MIAO Fasheng，LIAO Kang，ZHANG Longfei. Displacement prediction of landslides based on variational mode decomposition and GWO-MIC-SVR model. , 2018, 37(6): 1395-1406.

链接本文:

http://www.rockmech.org/CN/10.13722/j.cnki.jrme.2017.1508 或 http://www.rockmech.org/CN/Y2018/V37/I6/1395

[20]	DRAGOMIRETSKIY K，ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing，2014，62(3)：531-544.
[22]	MIRJALILI S，MIRJALILI S M，LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software，2014，69(3)：46-61.
[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))
[13]	LIAN C，ZENG Z，YAO W，et al. Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level[J]. Stochastic Environmental Research and Risk Assessment，2014，28(8)：1 957-1 972.
[23]	RESHEF D N，RESHEF Y A，FINUCANE H K，et al. Detecting novel associations in large datasets[J]. Science，2011，334(6062)：1 518-1 524.
[21]	VAPNIK V N. The nature of statistical learning theory[M]. New York：Springer，2000：138-167.
[1]	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))
[7]	HUANG F，HUANG J，JIANG S，et al. Landslide displacement prediction based on multivariate chaotic model and extreme learning machine[J]. Engineering Geology，2017，218：173-186.
[11]	林大超，安凤平，郭章林，等. 滑坡位移的多模态支持向量机模型预测[J]. 岩土力学，2011，32(增1)：451-458.(LIN Dachao，AN Fengping，GUO Zhanglin，et al. Prediction of landslide displacements through multimode support vector machine model[J]. Rock and Soil Mechanics，2011，32(Supp.1)：451-458.(in Chinese))
[17]	SAITO M. Research on forecasting the time of occurrence of slope failure[J]. Soil Mechanics and Foundation Engineering，1969，17：29-38.
[24]	孙广路，宋智超，刘金来，等. 基于最大信息系数和近似马尔科夫毯的特征选择方法[J]. 自动化学报，2017，43(5)：795-805.(SUN Guanglu，SONG Zhichao，LIU Jinlai，et al. Feature selection method based on maximum information coefficient and approximate markov blanket[J]. Acta Automatica Sinica，2017，43(5)：795-805.(in Chinese))
[4]	彭令，牛瑞卿，吴婷. 时间序列分析与支持向量机的滑坡位移预测[J]. 浙江大学学报：工学版，2013，47(9)：1 672-1 679.(PENG Ling，NIU Ruiqing，WU Ting. Time series analysis and support vector machine for landslide displacement prediction[J]. Journal of Zhejiang University：Engineering Science，2013，47(9)：1 672-1 679.(in Chinese))
[6]	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，2017，(3)：1-14.
[8]	HUANG F，YIN K，ZHANG G，et al. Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory[J]. Environmental Earth Sciences，2016，75(20)：1 376.
[10]	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))
[14]	周超，殷坤龙，黄发明. 混沌序列WA-ELM耦合模型在滑坡位移预测中的应用[J]. 岩土力学，2015，36(9)：2 674-2 680.(ZHOU Chao，YIN Kunlong，HUANG Faming. Application of the chaotic sequence WA-ELM coupling model in landslide displacement prediction[J]. Rock and Soil Mechanics，2015，36(9)：2 674-2 680.(in Chinese))
[16]	REN F，WU X，ZHANG K，et al. Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping Landslide in the Three Gorges，China[J]. Environmental Earth Sciences，2015，73(8)：4 791-4 804.
[18]	吴益平，滕伟福，李亚伟. 灰色-神经网络模型在滑坡变形预测中的应用[J]. 岩石力学与工程学报，2007，26(3)：632-636.(WU Yiping，TENG Weifu，LI Yawei. Application of grey-neural network model to landslide deformation prediction[J]. Chinese Journal of Rock Mechanics and Engineering，2007，26(3)：632-636.(in Chinese))
[5]	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，China[J]. Engineering Geology，2016，204：108-120.
[9]	LIAN C，ZENG Z，YAO W，et al. Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis[J]. Neural Computing and Applications，2014，24(1)：99-107.
[15]	李骅锦，许强，何雨森，等. WA联合ELM与OS-ELM的滑坡位移预测模型[J]. 工程地质学报，2016，24(5)：721-731.(LI Huajin，XU Qiang，HE Yusen，et al. Predictive modeling of landslide displacement by wavelet analysis and multiple extreme learning machines[J]. Journal of Engineering Geology，2016，24(5)：721-731.(in Chinese))
[19]	LI X，KONG J，WANG Z. Landslide displacement prediction based on combining method with optimal weight[J]. Natural Hazards，2012，61(2)：635-646.
[25]	卢书强，易庆林，易武，等. 库水下降作用下滑坡动态变形机理分析—以三峡库区白水河滑坡为例[J]. 工程地质学报，2014，22(5)：869-875.(LU Shuqiang，YI Qinglin，YI Wu，et al. Study on dynamic deformation mechanism of landslide in drawdown of reservoir water level—take Baishuihe Landslide in Three Gorges Reservoir area for example[J]. Journal of Engineering Geology，2014，22(5)：869-875.(in Chinese))
[2]	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))
[12]	LIAN C，ZENG Z，YAO W，et al. Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine[J]. Natural Hazards，2013，66(2)：759-771.
[26]	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.(YAO Wei，LIAN Cheng，CHENG Li. A dynamic probabilistic model for landslide displacement prediction[J]. Hydrogeology and Engineering Geology，2015，42(5)：134-139.(in Chinese))