## 5 Comparative Analysis
### Stability Test of the Displacement Data and Evaluation of Model Performance
#### 5.1.2 Evaluation of Model Prediction Performance
Among the conventional RNN and its variant models, the BiLSTM and the Attention-LSTM models were more prominent, especially the Attention-LSTM model. The better prediction performance of the LSTM and GRU models was due to their improvement of the long-term dependence problem of the RNN models. As shown in Figure 10, when using the \(R^{2}\) as the metric, the prediction accuracy of the Attention-LSTM model at five monitoring points of FJ004, FJ006, FJ009, FJ010, and FJ011 was much higher than that of the ordinary LSTM. As listed in Table 5, the values of the ADF test at FJ006, FJ010, and FJ011 were greater than 0.01, and the value at FJ004 was close to 0.01. Therefore, when the displacement data are more unsteady, the fitting performance of the Attention-LSTM model is more outstanding. The above-predicted results at seven monitoring points of the Huanglianshu landslide indicate that the introduction of the attention mechanism has an important influence on the nonlinear displacement data. The deformation of a slope is influenced by many factors, such as rainfall and weather changes, and the influence weight of these potential factors on slope deformation is reflected in the time-series displacement data. The conventional LSTM model cannot focus on the important parts of the displacement data. However, applying the attention mechanism to the LSTM model allows the neural network to adaptively focus on the local displacement data of input time-series that are more important to the current output. Therefore, the LSTM model with an attention mechanism was more effective for slope deformation prediction. As listed in Table 4, the values of the four prediction metrics of the transformer model on monitoring points FJ010 and FJ011 within the deformation area were significantly higher than other models, while the displacement data of monitoring points FJ003 and FJ005 outside the deformation area also had high prediction accuracy. This indicates that the transformer model has strong nonlinear processing ability, and the displacement within and outside the deformation area of the Huanglianshu landslide can be accurately predicted by the transformer model. The network structure of the transformer model is entirely composed of the self-attention mechanism. The self-attention mechanism is a variant of the attention mechanism, is less dependent on external information, and is better at capturing internal correlations between displacement data. The self-attention mechanism focuses on the important parts of the time-series data by assigning different weights, which reduces the degree of influence of abnormal points on the prediction results to improve the prediction accuracy. In addition, for some special slopes, such as the rainfall-induced landslides [43; 44], the landslide displacement is mainly influenced by the amount of rainfall; furthermore, the rainfall in various regions of the world shows obvious annual periodicity, which means that the displacement data of the slope have year-long dependency. Compared with the LSTM model, the transformer model had stronger long-term dependency modeling capabilities and has a better effect on long sequences. Therefore, the transformer model is better adapted to predict nonlinear landslide displacements affected by multiple factors.