基于时间序列分解和多变量混沌模型的滑坡阶跃式位移预测

黄发明; 殷坤龙; 杨背背; 李喜; 刘磊; 付小林; 刘小文

doi:10.3799/dqkx.2018.909

Volume 43 Issue 3

Mar. 2018

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Article Navigation > Earth Science > 2018 > 43(3): 887-898

Huang Faming, Yin Kunlong, Yang Beibei, Li Xi, Liu Lei, Fu Xiaolin, Liu Xiaowen, 2018. Step-Like Displacement Prediction of Landslide Based on Time Series Decomposition and Multivariate Chaotic Model. Earth Science, 43(3): 887-898. doi: 10.3799/dqkx.2018.909

Citation:

Huang Faming, Yin Kunlong, Yang Beibei, Li Xi, Liu Lei, Fu Xiaolin, Liu Xiaowen, 2018. Step-Like Displacement Prediction of Landslide Based on Time Series Decomposition and Multivariate Chaotic Model. Earth Science, 43(3): 887-898. doi: 10.3799/dqkx.2018.909

Citation:

Huang Faming, Yin Kunlong, Yang Beibei, Li Xi, Liu Lei, Fu Xiaolin, Liu Xiaowen, 2018. Step-Like Displacement Prediction of Landslide Based on Time Series Decomposition and Multivariate Chaotic Model. Earth Science, 43(3): 887-898. doi: 10.3799/dqkx.2018.909

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Step-Like Displacement Prediction of Landslide Based on Time Series Decomposition and Multivariate Chaotic Model

doi: 10.3799/dqkx.2018.909

1.
School of Civil Engineering and Architecture, Nanchang University, Nanchang 330000, China
2.
Geological Survey Institute, China University of Geosciences, Wuhan 430074, China
3.
China Institute of Geo-Environment Monitoring, Beijing 100081, China

Received Date: 2017-10-07
Publish Date: 2018-03-15

Abstract

Abstract

The GPS monitoring cumulative displacement on reservoir landslides in the Three Gorges Reservoir area shows step-like characteristics and is a probable chaotic time series under the influences of the seasonal rainfall and reservoir water level fluctuation. Traditionally, the uni-variable chaotic model is commonly used to predict the landslide displacement; and all existing multivariable models select the input variables empirically without theoretical exploration of the nonlinear dynamic evolution process of landslide displacement and its inducing factors. A new combined model based on double exponential smoothing (DES), multivariable chaotic model, extreme learning machine (ELM) is proposed in this study. First, the chaos characteristic of landslide displacement is identified by the combined DES and multivariable chaotic ELM. Second, the DES method is used to predict the cumulative displacement. The predictive results are the trend displacement, and the periodic displacement is obtained by reducing the trend displacement from the cumulative displacement. Third, the multivariate phase space reconstruction method of chaotic theory is used to explore the dynamic relationship between the periodic displacement and its inducing factors, and the ELM model is established to predict the periodic displacement. Finally, the total forecast cumulative displacement is obtained by adding the predictive trend and periodic displacement. The GPS monitoring cumulative displacement on the Baishuihe landslide is used as case study. In addition, the proposed model is compared with the combined DES and multivariable chaotic particle swarm optimized support vector machine model, the combined DES and uni-variable chaotic ELM model. The results show that the prediction accuracy of the proposed model is higher than that of other models. The proposed model explores the nonlinear characteristic of landslide displacement and its dynamic relationship with inducing factors. The model also reflects the physical meaning of the nonlinear evolution of the landslide displacement.
- reservoir landslide,
- step-like displacement prediction,
- chaos theory,
- multivariate phase space reconstruction,
- exponential smoothing method,
- extreme learning machine

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