Abstract: This paper develops a novel combined method of deformation forecasting for high embankment dam, considering the complex nonlinearity and non-stationarity of the time series, to improve the simulation of the long-term development trend and its fluctuation characteristics. Seasonal-trend decomposition based on the Loess smoothing is adopted to decompose the dam displacement time series into trend, seasonal, and remainder components. A long-short term memory neural network model is used to predict separately the three components that are then summed up to generate a displacement prediction. To evaluate and compare the prediction results quantitatively, three evaluation indicators are introduced and the results are compared with those of the SARIMA model, the LSTM neural network model, and a combined model of SARIMA-LSTM. New deformation model shows high accuracy and stability in the prediction. It is applicable to deformation with long-term trend and fluctuation characteristic with water level.

Key words: high embankment dam, deformation prediction, combined model, seasonal-trend decomposition, deep learning