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

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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

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	LI Linwei
	WU Yiping
	MIAO Fasheng
	LIAO Kang
	ZHANG Longfei

Cite this article:

LI Linwei,WU Yiping,MIAO Fasheng, et al. Displacement prediction of landslides based on variational mode decomposition and GWO-MIC-SVR model[J]. , 2018, 37(6): 1395-1406.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2017.1508 OR https://rockmech.whrsm.ac.cn/EN/Y2018/V37/I6/1395

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