## 4 Results
### Prediction Metrics and Accuracy  
In this paper, we used four metrics to evaluate the prediction performance of the eleven models for slope deformation: the mean absolute error (MAE), mean absolute percentage error (MAPE) [40], root mean square error (RMSE), and \(R^{2}\), which are defined in Table 3. The results of the prediction accuracy when using the above four metrics are shown in Table 4. The smaller the MAE or the RMSE, or the MAPE value, the higher the prediction accuracy of the model, and MAPE = 0 indicates a perfect model; the closer the value of \(R^{2}\) is to 1, the higher the prediction accuracy of the model.  
Figure 8: Predicted displacements and the autocorrelation functions of the seven monitoring points. (**a**) The autocorrelation functions; (**b**) the predicted and measured displacements.  
\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{9}{*}{MAE} & ARIMA & 2.7514 & **2.6987** & 1.7134 & 2.0778 & **4.5172** & 4.2154 & 2.5601 \\  & SVR & 3.1167 & 3.0527 & 1.9669 & 2.0236 & 4.5256 & 4.5768 & 3.2575 \\  & XGBoost & 3.2107 & 2.7615 & 1.9541 & 2.2226 & 5.6654 & 4.2474 & 2.4652 \\  & DNN & 3.4244 & 2.9413 & 1.8209 & 2.1979 & 5.6416 & 4.4444 & 3.0304 \\  & CNN & 2.9597 & 3.0787 & 1.7600 & 2.2304 & 6.1500 & 4.9373 & 2.6765 \\  & RNN & 2.8005 & 2.8538 & 1.7716 & 2.1441 & 4.7417 & 4.6331 & 2.6581 \\  & LSTM & 2.7278 & 3.0349 & 1.7750 & 2.0582 & 5.3751 & 4.6890 & 3.0843 \\  & BiLSTM & 2.7548 & 3.0843 & 1.8252 & 2.0733 & 5.1470 & 4.5105 & 2.5058 \\  & Attention- & 2.8236 & 3.0341 & 1.7601 & **2.0081** & 5.2749 & 4.6290 & **2.3208** \\  & LSTM & 2.6942 & 2.7917 & 1.7928 & 2.0549 & 4.8912 & 4.2850 & 2.5024 \\  & Transformer & **2.4117** & 3.0025 & **1.7117** & 2.0222 & 5.9667 & **3.9941** & 2.6732 \\ \hline \multirow{9}{*}{MAPE} & ARIMA & 0.1761 & **0.3937** & **0.1310** & 0.4546 & **0.0152** & 0.0113 & 0.0229 \\  & SVR & 0.1901 & 0.4882 & 0.1571 & 0.4632 & 0.0153 & 0.0122 & 0.0289 \\  & XGBoost & 0.2018 & 0.4364 & 0.1433 & 0.4997 & 0.0188 & 0.0114 & 0.0220 \\  & DNN & 0.2202 & 0.5330 & 0.1397 & 0.4914 & 0.0194 & 0.0120 & 0.0271 \\  & CNN & 0.1879 & 0.5294 & 0.1398 & 0.5062 & 0.0211 & 0.0133 & 0.0241 \\  & RNN & 0.1731 & 0.4448 & 0.1352 & 0.5038 & 0.0159 & 0.0123 & 0.0236 \\  & LSTM & 0.1674 & 0.5202 & 0.1340 & 0.4649 & 0.0180 & 0.0124 & 0.0275 \\  & BiLSTM & 0.1689 & 0.5408 & 0.1390 & 0.4733 & 0.0172 & 0.0120 & 0.0224 \\  & Attention- & 0.1735 & 0.5218 & 0.1305 & 0.4779 & 0.0181 & 0.0125 & **0.0208** \\  & GRU & 0.1698 & 0.4322 & 0.1375 & 0.4739 & 0.0165 & 0.0114 & 0.0223 \\  & Transformer & **0.1614** & 0.4946 & 0.1367 & **0.4444** & 0.0203 & **0.0106** & 0.0238 \\ \hline \multirow{9}{*}{RMSE} & ARIMA & 4.2655 & **3.8868** & 2.5959 & 2.9387 & **7.2359** & 6.9043 & 4.4374 \\  & SVR & 4.6544 & 4.0596 & 2.7387 & 2.8294 & 7.4112 & 7.3795 & 5.7565 \\  & XGBoost & 5.5068 & 4.1025 & 3.0221 & 3.2335 & 9.0456 & 6.8872 & 4.2473 \\  & DNN & 4.8558 & 4.0541 & 2.5911 & 3.0223 & 8.1783 & 6.4410 & 4.8310 \\  & CNN & 4.6576 & 4.1445 & 2.6057 & 3.0068 & 8.5456 & 6.7313 & 4.5103 \\  & RNN & 4.3923 & 3.9846 & 2.5707 & 2.9604 & 7.4221 & 6.8591 & 4.4002 \\  & LSTM & 4.3140 & 4.0351 & **2.5216** & 2.9264 & 8.2447 & 7.054