关键词: 大坝变形, 变形预测, 集合经验模态分解, 主成分分析, 自回归移动平均模型

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