Slope displacement prediction method based on EEMD-PSO-ELM model

doi:10.16265/j.cnki.issn1003-3033.2020.03.024

Abstract

Abstract: In order to solve the difficulty in predicting slope deformation and displacement, Beidou monitoring system was used to obtain slope displacement data, and EEMD method, PSO and ELM were introduced to build an EEMD-PSO-ELM model for displacement prediction. Then, with No. 5 monitoring point of Pangang Group limestone mine as an example, original data was denoised wavelet by, and displacement time series were decomposed into fluctuating displacement and trending displacement by EEMD method. Displacement in next period was predicted using PSO-ELM optimization model. Finally, cumulative displacement prediction of slope was obtained by combining the two results, and they were compared and analyzed. The research shows that the mean relative error(MRE), root mean square error(RMSE) and goodness of fit of EEMD-PSO-ELM model are 0.15%, 0.03 and 0.9999 respectively, indicating the model has certain accuracy and applicability.

Key words: displacement prediction of mine slope, ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO), extreme learning machine (ELM), wavelet denoising

CLC Number:

X928.03

XIE Bo, SHI Fuqiang, LIAO Xueyan, MA Sheng, YANG Wei, LU Xiangxiang. Slope displacement prediction method based on EEMD-PSO-ELM model[J]. China Safety Science Journal, 2020, 30(3): 157-162.

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