基于地表监测数据和非线性时间序列组合模型的滑坡位移预测

doi:10.13722/j.cnki.jrme.2016.1534

Abstract
Figure/Table
References (25)
Related Citation (15)

Download: PDF (579 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

According to the nonlinear characteristic of displacement-time curve of landslide and deficiency of traditional prediction models，the combination model based on surface monitoring data and nonlinear time series analysis was proposed to predict landslide displacement. Taking Xintan landslide and Sanzhouxi landslide for example，deformation characteristics and influence factors were researched by analyzing information of displacement，rainfall and reservoir water level and so on. On the basis of testing trend and periodic characteristics of landslide displacement by using athwart order method and wavelet analysis，nonlinear combination model was used to predict it. Trend item displacement was fitted and predicted by polynomial function. Using a trigonometric function based on wavelet analysis(WA-TF model) to predict periodic item displacement. BP neural network whose parameters were optimized by genetic algorithm was used to predict random item displacement. The accumulative displacement was obtained by adding up all items displacement and it was compared and analyzed with the monitoring values. The results indicate that nonlinear combination model has high accuracy and good versatility. This provides a possible thinking for quantitative prediction of landslide displacement.

Key words： slope engineering landslide displacement prediction time series nonlinear combination model wavelet analysis genetic algorithm BP neural network

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	GUO Zizheng1
	YIN Kunlong1
	HUANG Faming2
	LIANG Xin1

Cite this article:

GUO Zizheng1,YIN Kunlong1,HUANG Faming2, et al. Landslide displacement prediction based on surface monitoring data and nonlinear time series combination model[J]. , 2018, 37(S1): 3392-3399.

URL:

https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2016.1534 OR https://rockmech.whrsm.ac.cn/EN/Y2018/V37/IS1/3392

[1] 刘勇，余宏明，钟苹，等.滑坡预测的计算智能方法[M]. 武汉：中国地质大学出版社，2012：5.(LIU Yong，YU Hongming，ZHONG Ping，et al. The intelligent computation method of the prediction of landslide[M]. Wuhan：China University of Geosciences Press，2012：5.(in Chinese))

[2] 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.(QU Qiang，HUANG Runqiu，LI Xiuzhen. Research progress on the landslide time prediction[J]. Advanced in Earth Science，2004，19(3)：478–483.(in Chinese))

[3] LIAN C，ZENG Z G，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.

[4] CAI，Z，XU，W，MENG，Y. et al. Prediction of landslide displacement based on GA-LSSVM with multiple factors[J]. Bulletin of Engineering Geology and the Environment，2016，75(2)：637–646.

[5] LIU Z，SHAO J，XU W，et al. Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches[J]. Landslides，2014，11(5)：889–896.

[6] 周超，殷坤龙，曹颖，等. 基于诱发因素响应与支持向量机的阶跃式滑坡位移预测[J]. 岩石力学与工程学报，2015，34(增2)：4 132–4 139.(ZHOU Chao，YIN Kunlong，CAO Ying，et al. Displacement prediction of step-like landslide based on the response of inducing factors and support vector machine[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(Supp.2)：4 132–4 139.(in Chinese))

[7] 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.

[8] 张俊，殷坤龙，王佳佳，等. 基于时间序列与PSO-SVM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))

[9] 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))

[10] 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))

[11] HUANG F M，YIN K L，HE T，et al. Analysis of influence factors and displacement prediction of reservoir landslide-a case study of three gorges reservoir，China[J]. Technical Gazette，2016，23(2)：617–626.

[12] 张正虎，袁孟科，邓建辉，等. 基于改进灰色-时序分析时变模型的边坡位移预测[J]. 岩石力学与工程学报，2014，33(增2)：3 791–3 797.(ZHANG Zhenghu，YUAN Mengke，DENG Jianhui，et al. Displacement prediction of slope based on improved grey-time series time-varying model[J]. Chinese Journal of Rock Mechanics and Engineering，2014，33(Supp.2)：3 791–3 797.(in Chinese))

[13] 宋克志，王梦恕，宋克勇. 边坡位移预测的神经网络模型研究[J]. 岩石力学与工程学报，2003，22(增1)：2 382–2 385.(SONG Kezhi，WANG Mengshu，SONG Keyong. Study on NNT model for slope displacements prediction[J]. Chinese Journal of Rock Mechanics and Engineering，2003，22(Supp.1)：2 382–2 385.(in Chinese))

[14] 沈强，陈从新，汪稔. 边坡预测的RBF神经网络方法[J]. 岩石力学与工程学报，2006，25(增1)：2 882–2 887.(SHEN Qiang，CHEN Congxin，WANG Ren. Method of forecast displacement of slope based on RBF neural network[J]. Chinese Journal of Rock Mechanics and Engineering，2006，25(Supp.1)：2 882–2 887.(in Chinese))

[15] 刘永健，张伯友. 混沌时间序列在边坡位移预测中的应用[J]. 辽宁工程技术大学学报，2007，26(1)：74–76.(LIU Yongjian，ZHANG Boyou. Application of chaotic time-series in slope displacement forecasting[J]. Journal of Liaoning Technical University，2007，26(1)：74–76.(in Chinese))

[16] CHEN H Q，ZENG Z G. Deformation prediction of landslide based on improved back-propagation neural network[J]. Cognitive Computation，2013，5(1)：56–62.

[17] 缪海波，殷坤龙，柴波，等. 基于非平稳时间序列分析的滑坡变形预测[J]. 地质科技情报，2009，28(4)：107–111.(MIAO Haibo，YIN Kunlong，CHAI Bo，et al. Deformation prediction of landslide based on non-stationary time series[J]. Geological Science and Technology Information，2009，28(4)：107–111.(in Chinese))

[18] 卢理. 时间序列加法模型的分解预测研究[硕士学位论文][D]. 成都：西南交通大学，2007.(LU Li. Research of time series addition model decomposition-forecasting[M. S. Thesis][D]. Chengdu：Southwest Jiaotong University，2007.(in Chinese))

[19] 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 Science，2016，75(20)：1 376–1 393.

[20] 杨叔子，吴雅. 时间序列分析的工程应用：下册[M]. 武汉：华中科技大学出版社，1992：293–304.(YANG Shuzi，WU Ya. Engineering application of time series analysis：the second volume[M]. Wuhan：Huazhong University of Science and Technology Press，1992：293–304.(in Chinese))

[21] 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.

[22] HOLLAND J H. Adaptation in natural and artificial system[J]. Quarterly Review of Biology，1975，6(2)：126–137.

[23] 杨成详，冯夏庭. 滑坡非线性演化行为的自组织进化识别[J]. 岩石力学与工程学报，2005，24(6)：911–914.(YANG Chengxiang，FENG Xiating. Evolutionary self-organizing identification of nonlinear dynamics of landslides[J]. Chinese Journal of Rock Mechanics and Engineering，2005，24(6)：911–914.(in Chinese))

[24] 熊光赤. 人工智能在边坡中的应用[硕士学位论文][D]. 昆明：昆明理工大学，2006.(XIONG Guangchi. Application of artificial intelligence in slope engineering[M. S. Thesis][D]. Kunming：Kunming University of Science and Technology，2006.(in Chinese))

[25] 黄志全，樊敬亮，王思敬. 混沌时间序列预测的局域法在边坡变形分析中的应用[J]. 工程地质学报，2005，13(2)：252–256.(HUANG Zhiquan，FAN Jingliang，WANG Sijing. A prediction method of chaotic time series for slope deformation[J]. Journal of Engineering Geology，2005，13(2)：252–256.(in Chinese))