# Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model
## 4 Empirical Analysis
### Comparative Analysis of Tailings Pond Risk Prediction Experiments  
In this experiment, tailings pond dam failure risk prediction models were constructed based on EEMD-DA-LSTM, EEMD-LSTM, and DA-LSTM, respectively, the prediction results of different models were compared and analysed for the relevant data of valley-type tailings pond WKB-1 and the relevant data of mountain adjacent-type tailings pond WKB-2, and the mean absolute error, the root mean square error, and the mean absolute percentage error were used to assess the prediction accuracy of the models. The average absolute error, root mean square error, and average absolute percentage error were used to verify the reliability of the method proposed in this chapter. The safety monitoring data, such as dip line, rainfall, reservoir level, and displacement, from September 2020 to October 2020 of valley-type tailings pond WKB-1 were selected as a dataset with a total of 997 data points. The change curve of displacement in dataset WKB-1 is shown in Figure 6.  
Comparisons of the prediction results of each prediction model based on dataset WKB-1 for the displacement of measurement point J1 are shown in Figures 7 and 8. From the prediction results of the DA-LSTM model in Figure 7 and the EEMD-DA-LSTM model, it can be seen that the prediction effect of the DA-LSTM model is poorer, although it can achieve the basic fitting of the trend of the displacement change, but the prediction effect is not good compared with the EEMD-DA-LSTM model when the displacement changes suddenly. Compared with the prediction results of the DA-LSTM model in Figure 7, the prediction effect of the EEMD-LSTM model in Figure 8 is better and it can accurately predict the change in the risk of tailings pond failure when the displacement undergoes a strong nonlinear change, but the prediction accuracy still needs to be further improved. Therefore, the combination of the EEMD method and DA-LSTM model was considered to predict the risk of tailings pond dam failure. From the prediction results of the EEMD-DA-LSTM model in Figure 8, it can be seen that the EEMD-DA-LSTM model can well capture the strong nonlinear changes in the displacement of the tailings dam and the error between the model predicted value and the measured value of the displacement is small. As shown in Table 5, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the DA-LSTM model are reduced by 0.076 mm, 1.072%, and 0.044 mm, respectively, compared to the LSTM model, and the combination of dual attention and the LSTM network model can reduce the prediction error of the LSTM model and improve model prediction accuracy. Compared with the LSTM model, the various error indicators of the EEMD-LSTM model are reduced by 0.153 mm, 4.541%, and 0.133 mm, respectively, which indicates that the smoothing of the displacement time series data using EEMD can effectively improve the reliability of LSTM model prediction. Compared with the single LSTM model, the combined DA-LSTM model, and the combined EEMD-LSTM model, the mean absolute error (MAE) of the EEMD-DA-LSTM model is 0.159 mm, which is a decrease of 0.186 mm, 0.110 mm, and 0.033 mm, respectively; the mean absolute percentage error (MAPE) is 5.587%, which is a decrease of 5.278%, 4.206%, and 0.737%, respectively; and the root mean square error (RMSE) is 0.197 mm, decreasing by 51.7%, 45.9%, and 28.4%, respectively. The results show that for the strong nonlinearity and random volatility of tailings dam deformation and the complex coupling between dam failure risk factors, the dam failure risk prediction method based on EEMD-DA-LSTM proposed in this paper is able to reduce the prediction error of the LSTM model for tailings dam failure risk and effectively improve the prediction accuracy of the EEMD-LSTM model. Figure 8: Comparison of EEMD-LSTM model and EEMD-DA-LSTM model.