关键词: 滑坡位移预测, 时间序列, 经验模态分解, 长短时记忆网络, 新滩滑坡, 三峡库区

Abstract: It is very important to establish a high-precision displacement prediction model for the advance prediction of landslides. However, static prediction models were used in most of the previous studies, which can not meet the need of dynamic characteristics of landslides. For this reason, taking Xintan landslide in the Three Gorges Reservoir area as an example, and the Long Short-Term Memory(LSTM) was selected to dynamically predict the cumulative displacement before sliding. Firstly, the Empirical Mode Decomposition method(EMD) was used to decompose the cumulative displacement of landslide into trend term and period term. Then the polynomial function was used to predict the displacement of trend term, and the dynamic Long Short-Term Memory neural network model(LSTM) was used to predict the displacement of period term. Finally, the displacement of each component was accumulated to obtain the final model calculation value. The results showed that the root mean square error of the prediction results of the model was 17.07 mm, the correlation coefficient was 0.999, which had a good prediction accuracy and provided a feasible idea for the prediction of the ladder-like landslide displacement.

Key words: landslide displacement prediction, time series, Empirical Mode Decomposition(EMD), Long Short-Term Memory(LSTM), Xintan landslide, Three Gorges Reservoir area;