The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-3/W10
08 Feb 2020
 | 08 Feb 2020


T. W. Chen, Q. D. Duan, X. D. Zheng, R. Gan, and M. Pan

Keywords: Dam deformation, Deformation prediction, EEMD, SARIMA, Seasonality, Periodicity

Abstract. There are many factors affecting dam deformation, and the time series of deformation data is directly modeled without considering the seasonality and periodicity of each influencing factor, the Ensemble Empirical Mode Decomposition (EEMD) and the Seasonal Autoregressive Integrated Moving Average (SARIMA) is proposed for prediction in this paper. Firstly, the time series of deformation data is decomposed by EEMD, which weakens its volatility to some extent, and decomposes various factors affecting dam deformation, so as to obtain a series of Intrinsic Mode Function (IMF) with different frequencies; secondly, according to the seasonal characteristics and periodic characteristics of each IMF, the SARIMA model was established respectively for rolling prediction; thirdly, the final forecast results can be obtained by superimposing the forecast results of each IMF. It is verified by experiments and compared with Gray Model, Kalman Filter Model and SARIMA model that EEMD-SARIMA model has higher prediction accuracy, and it has better fitting degree, which means that it is an effective method for dam deformation prediction.