## 6 Discussion  
Considering predicting total displacement, the LSTM models showed a more satisfactory performance than the SVR models in ZK2-1 and ZK2-3, respectively, and the proposed Ensemble models yield relatively better results (Tables 8 and 9). In predicting trend terms, the LSTM models showed better performance than SVR models in ZK2-3 and worse performance in ZK2-1. In predicting periodic terms, the LSTM models showed better performance than SVR models in ZK2-1 and worse performance in ZK2-3. This suggests that the accuracy of the total displacement prediction does not depend in the case of the Shengjibao landslide on the accuracy of only one sub-sequence. Meanwhile, the generalization ability of the models directly affects the performance of the test dataset. The models established based on the performance on the validation dataset do not always perform as expected on the predicting dataset. As shown in Tables 6 and 10, the LSTM models did not achieve the expected performance in predicting the trend term of ZK2-1 or the periodic term of ZK2-3, while the SVR model did not achieve the expected performance in predicting the trend term of ZK2-3. There have been few reports on the research of decomposing the original landslide displacement into trend term, periodic term, and stochastic term [7,9] up to now. The stochastic displacement is caused by some random factors such as wind load and vehicle load. In decomposing original displacement, we chose to ignore the stochastic term. One should consider means to develop optimal models for predicting the stochastic displacement in the future. Additionally, the stability of the location of the GPS monitoring control point is the key controlling factors of the reliability of the monitoring data. Thus, the evaluation of the stability of the GPS monitoring control point is an important content that cannot be ignored in the processing of displacement observation data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**RMSE in Trend Term(mm)**} & \multicolumn{2}{c}{**RMSE in Periodic Term(mm)**} \\ \cline{2-5}  & **ZK2-1** & **ZK2-3** & **ZK2-1** & **ZK2-3** \\ \hline LSTM & 2.84 & 36.29 & 14.95 & 56.30 \\ SVR & 3.39 & 35.58 & 15.11 & 60.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy of the prediction in trend term and periodic term of ZK2-1 and ZK2-3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**Predicted**} & \multirow{2}{*}{**Optimal**} & \multirow{2}{*}{**Absolute Error**} & \multirow{2}{*}{**Relative Error**} \\  & **Displacement** & & & & \\  & **(mm)** & & & & \\ \hline
2015-01 & 2222.73 & 2216.74 & 0.93060 & 5.88 \({}^{\ast}\) & 0.26 \\
2015-02 & 2225.82 & 2223.06 & 1.0 & 2.75 \({}^{\ast}\) & 0.12 \\
2015-03 & 2232.97 & 2228.46 & 0.97188 & 4.47 \({}^{\ast}\) & 0.20 \\
2015-04 & 2236.89 & 2255.25 & 0.65265 & 18.53 \({}^{\ast}\) & 0.83 \\
2015-05 & 2277.32 & 2271.63 & 0.14902 & 5.61 \({}^{\ast}\) & 0.25 \\
2015-06 & 2342.75 & 2350.48 & 1.0 & 7.73 \({}^{\ast}\) & 0.33 \\
2015-07 & 2401.47 & 2391.93 & 1.0 & 9.54 \({}^{\ast}\) & 0.40 \\
2015-08 & 2432.98 & 2428.25 & 0.96782 & 4.86 \({}^{\ast}\) & 0.20 \\
2015-09 & 2450.97 & 2435.83 & 0 & 14.80 \({}^{\ast}\) & 0.60 \\
2015-10 & 2463.37 & 2448.69 & 1.0 & 14.68 \({}^{\ast}\) & 0.60 \\
2015-11 & 2465.66 & 2453.88 & 1.0 & 11.78 \({}^{\ast}\) & 0.48 \\
2015-12 & 2477.34 & 2463.61 & 1.0 & 13.73 \({}^{\ast}\) & 0.55 \\ \hline Min & & & & 2.75 & 0.12 \\ Max & & & & 18.53 & 0.83 \\ Mean & & & & 9.53 \({}^{\ast,\ast}\) & 0.40 \\ RMSE & & 10.70 \({}^{\ast,\ast}\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy of the predicted displacement with the LSTM and SVR models of ZK2-3. As stated in Section 3.2, there is a sharp increase in the displacement rates in August and September 2013.