International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 1091–1097, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-1091-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 1091–1097, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-1091-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  08 Feb 2020

08 Feb 2020

DAM DEFORMATION PREDICTION BASED ON EEMD-SARIMA MODE

T. W. Chen1,2, Q. D. Duan1,2, X. D. Zheng1,2, R. Gan1,2, and M. Pan1,2 T. W. Chen et al.
  • 1College of Geomatic Engineering and Geoinformatics, Guilin University of Technology, 12 Jiangan Road, Guilin 541004, China
  • 2Guangxi Key Laboratory of Spatial Information and Geomatics, 12 Jiangan Road, Guilin 541004, China

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.