JOURNAL OF YANGTZE RIVER SCIENTIFIC RESEARCH INSTI ›› 2020, Vol. 37 ›› Issue (3): 57-63.DOI: 10.11988/ckyyb.20181146


Dam Deformation Prediction Using EEMD-PCA-ARIMA Model

ZHENG Xu-dong1,2, CHEN Tian-wei1,2, WANG Lei1,2, DUAN Qing-da1,2, GAN Ruo1,2   

  1. 1.College of Geomatic Engineering and Geoinformatics, Guilin University of Technology, Guilin 541004, China;
    2.Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China
  • Received:2018-10-26 Online:2020-03-01 Published:2020-05-09

Abstract: The trend of actual dam deformation curve is easily covered up by noises in dam observation data. In this paper, a prediction method for dam deformation integrating ensemble empirical mode decomposition (EEMD), principal component analysis (PCA) and autoregressive integrated moving average (ARIMA) model is put forward. The mapping matrix is constructed by EEMD and PCA of the observed data, and then the sample matrix constructed by the original data is transformed by the mapping matrix to accomplish denoising; subsequently, the ARIMA model of the processed observation data is built and employed to predict and analyze the observed data of horizontal displacement of dam crest. The prediction result is compared with the measured data, ARIMA predicted data directly removed of high-frequency components, ARIMA predicted data, and BP neural network predicted data. Results suggest that the proposed method is effective for dam deformation prediction as it could better acquire the actual deformation curve.

Key words: dam deformation, deformation prediction, EEMD, PCA, ARIMA

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