## 5 Comparative Analysis
### Stability Test of the Displacement Data and Evaluation of Model Performance
#### 5.1.2 Evaluation of Model Prediction Performance  
According to Table 4, we evaluated the prediction performance of the eleven models when using the four different evaluation metrics in Figure 9. The evaluation results were as follows:  
1. [label=(0)]
2. \(R^{2}\) reflected the overall fit of the prediction models to the real displacement data. As shown in Figure 9, when using \(R^{2}\) as the evaluation index, all models had the highest prediction accuracies at the monitoring points FJ003 and FJ009, while the prediction accuracies at monitoring points FJ004, FJ005, FJ006, and FJ011 were relatively low. Specifically, monitoring point FJ006 had the lowest prediction accuracy. As a whole, except for the monitoring point FJ006, the \(R^{2}\) of all other monitoring points reached more than 0.5, which showed that, after large-scale local sliding of the slope, the displacement data both within the deformation area and its surrounding area had a certain linear or nonlinear change characteristic, i.e., the slope deformation could be learned to achieve accurate prediction through a specific model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metric** & **Model** & **FJ003** & **FJ004** & **FJ005** & **FJ006** & **FJ009** & **FJ010** & **FJ011** \\ \hline \multirow{8}{*}{\(R^{2}\)} & ARIMA & 0.8423 & **0.5088** & 0.5170 & 0.3253 & **0.8759** & 0.5672 & 0.4649 \\  & SVR & 0.8122 & 0.4642 & 0.4625 & 0.3746 & 0.8698 & 0.5056 & 0.0995 \\  & XGBoost & 0.7371 & 0.4528 & 0.3454 & 0.1832 & 0.8061 & 0.5693 & 0.5098 \\  & DNN & 0.7956 & 0.4656 & 0.5188 & 0.2864 & 0.8415 & 0.6233 & 0.4057 \\  & CNN & 0.8119 & 0.4415 & 0.5134 & 0.2937 & 0.8269 & 0.5886 & 0.4472 \\  & RNN & 0.8327 & 0.4838 & 0.5264 & 0.3153 & 0.8695 & 0.5728 & 0.4738 \\  & LSTM & 0.8386 & 0.4706 & **0.5443** & 0.3310 & 0.8389 & 0.5482 & 0.4464 \\  & BiLSTM & 0.8365 & 0.4923 & 0.5240 & 0.3320 & 0.8532 & 0.5967 & 0.4895 \\  & Attention- & LSTM & 0.8343 & 0.4906 & 0.5364 & **0.4067** & 0.8674 & 0.6103 & **0.5149** \\  & GRU & 0.8342 & 0.4813 & 0.5220 & 0.3621 & 0.8625 & 0.6128 & 0.5036 \\  & Transformer & **0.8635** & 0.4940 & 0.5187 & 0.3768 & 0.8301 & **0.6382** & 0.4936 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\) \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Monitoring** & **FJ003** & **FJ004** & **FJ005** & **FJ006** & **FJ009** & **FJ010** & **FJ011** \\ \hline \hline \(p\)-value & 0.000071 & 0.009987 & 0.006490 & 0.013365 & 0.002463 & 0.074062 & 0.797659 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the ADF test for time-series displacement data at 7 monitoring points. 2. Upon combing through the results of the four evaluation metrics in Figure 9, it can be concluded that the optimal prediction models at each monitoring point were the same when using the MAE and MAPE as evaluation metrics, and the optimal prediction models at each monitoring point were the same when using the \(R^{2}\) and RMSE as evaluation metrics. In summary, the ARIMA, the Attention-LSTM, and the transformer model had higher prediction accuracies. In order to compare the accuracy differences of the models for the displacement data within and outside the deformation area, we list the best and worst prediction models at the seven monitoring points of the Huanglianshu landslide in Table 6. Table 6 indicated that, although the ARIMA model performed with outstanding prediction accuracy for the displacement data outside the deformation area, it did not perform as well as the transformer and Attention-LSTM models for the displacement data inside the deformation area. Second, the traditional machine learning models XGBoost and SVR had the worst prediction accuracies. The ARIMA statistical method was effective for predicting short-term stationary time-series data. In this paper, we employed the displacement data of the next two months for prediction, which belonged to the range of short-term prediction; and the prediction results proved the ability of the ARIMA model to predict short-term problems.