# Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model
## 4 Empirical Analysis
### Comparative Analysis of Tailings Pond Risk Prediction Experiments
The safety monitoring data of displacement, reservoir water level, and dry beach length from 4 March 2021 to 18 April 2021 for certain side hill-type tailings pond WKB-2 were selected, with a total of 620 data points. The variation curves of displacement in dataset WKB-2 are shown in Figure 9.  
The deformation data of tailings pond WKB-2 were also characterised by strong nonlinearity and frequent fluctuations. In this experiment, dataset WKB-2 was divided into the 80% training set and 20% test set. The single LSTM model, the DA-LSTM combined model, and the EEMD-LSTM combined model were compared and analysed with the EEMD-DA-LSTM model constructed in this paper, respectively. The relevant parameter settings of each model are shown in Table 6. At this time, the prediction performance of each model is the best. A comparison of the results of the model for predicting the dam failure risk of tailings pond WKB-2 is shown in Figure 9. As can be seen from Figure 10a, the LSTM model also suffers from poor prediction of strong nonlinear displacement variations when predicting the risk of dam failure for the Pongshan-type tailings pond. From Figure 10b, it can be seen that the DA-LSTM model is able to fit the tailings impoundment displacement changes better, capturing most of the strong nonlinear changes in the displacement sequence. From Figure 10a,c, it can be seen that the EEMD-LSTM model is able to better fit the variation of tailings pond displacement compared to the LSTM model. From Figure 10b,c, it can be seen that the prediction results of the DA-LSTM model are better than the EEMD-LSTM model compared to the experimental results on dataset WKB-1, which may be due to the fact that the amount of data in dataset WKB-2 had been reduced compared to that in dataset WKB-1, which improved the learning ability of the DA-LSTM model for the strong \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Parameters**} \\ \cline{2-5}
**Model** & \begin{tabular}{c} **Number of** \\ **Iterations** \\ \end{tabular} & \begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Hidden Layers** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **in LSTM Layer** \\ \end{tabular} \\ \hline LSTM & 200 & 0.001 & 1 & 100 \\ DA-LSTM & 256 & 0.005 & 2 & (128, 50) \\ EEMD-LSTM & 200 & 0.001 & 1 & 128 \\ EEMD-DA-LSTM & 100 & 0.02 & 3 & (100, 50, 20) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameter settings for each model. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Evaluation Indicators**} \\ \cline{2-4}
**Model** & \begin{tabular}{c} _MAE/mm_ \\ \end{tabular} & \
\begin{tabular}{c} _MAPE/\%_ \\ \end{tabular} &
\begin{tabular}{c} _RMSE/mm_ \\ \end{tabular} \\ \hline EEMD-DA-LSTM & 0.159 & 5.587 & 0.197 \\ EEMD-LSTM & 0.192 & 6.324 & 0.275 \\ DA-LSTM & 0.269 & 9.793 & 0.364 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: WKB-1 model error evaluation index calculation results. Figure 9: Displacement variation curve for tailings pond WKB-2. nonlinear displacement series. From Figure 10, it can be seen that the prediction results of the combined EEMD-DA-LSTM model are basically consistent with the displacement variations, and the fitting effect on displacement variations is better than that of the single LSTM model, the combined DA-LSTM prediction model, and the combined EEMD-LSTM prediction model. To further evaluate the accuracy of the prediction of each model, the prediction error of each model was calculated, and the results are shown in Table 7. As can be seen from the table, compared with the LSTM model, the _MAE_ of the EEMD-DA-LSTM model is 0.162 mm, which is a reduction of 0.293 mm, the _MAPE_ is reduced by 6.790%, and the _RMSE_ is reduced by 62.0%. Compared with the DA-LSTM model, the _MAE_ of the EEMD-DA-LSTM model is reduced by 17.8%, the _MAPE_ is 5.594%, a reduction of 0.289%, and the _RMSE_ is reduced by 19.6%, which indicates that the displacement was smoothed using the EEMD method.