## 5 Comparative Analysis
### Stability Test of the Displacement Data and Evaluation of Model Performance
#### 5.1.2 Evaluation of Model Prediction Performance
However, the ARIMA utilized a traditional linear method, and it is difficult to capture the nonlinear relationships between the displacement data of slopes. As is shown in Table 6, the prediction accuracy of the ARIMA on the nonstationary displacement data at the monitoring points of FJ010 and FJ011 within the deformation area was much lower than that of most machine learning models, which suggests that ARIMA is better suited to predicting more stable displacement data at monitoring points outside the deformarion area. However, for the actual time-series displacement data of slopes, because of the various uncertain factors, such as weather changes and rainfall, the original data were normally nonstationary. Therefore, the ARIMA model was actually not the best choice for slope deformation prediction in most cases. The prediction performance of the SVR model was closely related to the selection of the model kernel function. As mentioned in Section 3.4.2, we adopted three kernel functions to predict the displacement data at each monitoring point separately. The calculation results show that the displacement data at monitoring points FJ006, FJ010, and FJ011 were suitable for the RBF kernel function, and other monitoring points were suitable for the linear kernel function. As is shown in Table 3, the SVR model achieved more accurate prediction results for the displacement data of monitoring points FJ006, FJ010, and FJ011, which indicates that the SVR was better at handling nonstationary displacement data within the deformation area than the stationary displacement data outside the deformation area. Moreover, the traditional machine learning models SVR and XGBoost have outlier prediction values. As shown in Figure 10, the SVR model had outlier prediction values that deviated seriously from the real situation at monitoring point FJ011 (Figure 10a), and the XGBoost model had outlier prediction values at monitoring point FJ003 (Figure 10b). Although the SVR model and XGBoost model can roughly predict the changing trend of displacement data, they have difficulty capturing unusual fluctuations in the data. In other words, the SVR and XGBoost models have more stable performance fitting the data in the normal range, while the ARIMA model is better than the SVR and XGBoost for outlier fitting. Therefore, when the displacement data have noise, the traditional machine models SVR and XGBoost would be more sensitive to overfitting. For the deep learning models, overall, the conventional RNN and its variant LSTM, GRU, BiLSTM, and Attention-LSTM models achieved more accurate predictions compared with the conventional DNN model and CNN model, and the RNN variant model had better prediction performance than the conventional RNN at the monitoring points within and outside the deformation area. In a conventional RNN model and its variant models, the network structure can store the information on each neuron; whereas in a conventional DNN, information only passes through the network in the forward direc  
Figure 10: Outlier prediction values of SVR and XGBoost model. (**a**) Outlier prediction values of SVR. (**b**) Outlier prediction values of XGBoost. tion. As mentioned in Section 4.2, there was a first-order autocorrelation between the before and after data in the time-series displacement data of the Huanglianshu landslide; thus, the conventional RNN model and its variant models were more suitable for slope deformation prediction. Among all deep learning models, the CNN model employed in this paper accomplished the lowest prediction accuracy at all monitoring points. Actually, the CNN method was placed before the input of the LSTM model for the first step to reduce the features [24; 42] in most applications. The CNN method was used to capture the deep features in the data, and then the LSTM method was adopted to extract the time-series features in the data. Therefore, using a single CNN model is not suitable for predicting slope deformation.