# Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model
## 3 Risk Prediction Model for Tailings Ponds  
Based on the EEMD-LSTM model [12], combined with dual attention, the tailings dam failure risk prediction method of EEMD-DA-LSTM is proposed to achieve the quantisation of the input features, select the most relevant tailings dam failure risk factors, and predict the deformation of the tailings dam. Aiming at the different degrees of influence of tailings pond failure risk factors on tailings pond deformation and the problem that LSTM is prone to losing some information in long time series prediction, a tailings pond failure risk prediction method based on EEMD-DA-LSTM is proposed. The method takes into account the strong nonlinear characteristics of the data, temporal characteristics, and multifactor coupling and uses dual attention to fully explore the correlation between the deformation of the tailings pond and its influencing factors, as well as the correlation between the risk factors of tailings pond dam failure in the historical time series. The prediction results of each model are analysed by experimental comparison and the prediction accuracy of each model is assessed according to the error evaluation index. The weights of the input features are dynamically adjusted through dual attention to improve the memory ability of the LSTM network for temporal information, thus improving the accuracy and credibility of the LSTM model prediction. The specific prediction flow of the tailings dam failure risk prediction method based on EEMD-DA-LSTM is shown in Figure 1.  
The process of tailings pond dam failure risk prediction based on EEMD-DA-LSTM is described as follows:  
(1) Pearson correlation coefficients are used to analyse the correlation between the time series data of tailings pond dam failure risk factors, screen the main influence factors of tailings dam deformation (F\({}_{1}\), F\({}_{2}\), \(\ldots\), F\({}_{k}\)), and reduce the dimensionality of model input features. (2) An ensemble empirical modal decomposition method was used to smooth the tailings pond displacement time series data and decompose it into a number of J1 measurement point displacement components, [IMF\({}_{1}\), IMF\({}_{2}\), \(\ldots\), IMF\({}_{N}\), Res]. (3) The displacement subsequence of the J1 measurement point obtained from the EEMD decomposition was combined with the time series data of the main influence factors of tailings dam deformation, respectively, [IMF\({}_{1}\), F\({}_{1}\), F\({}_{2}\), \(\ldots\), F\({}_{k}\)], as model inputs. DA-LSTM prediction models are constructed respectively and parameters such as learning rate, iteration number, and training batch of the model are set for model training to seek the optimal network structure and predict the change in the displacement component of the J1 measurement point. (4) The DA-LSTM model prediction results are superimposed and reconstructed, and the FA-LSTM layer adopts feature attention to encode the quantitative weights and extract the feature information related to the output displacement at each time step in the LSTM network. The TA-LSTM layer adopts the temporal attention to fully excavate the historical temporal sequence state information related to the displacement change at the current moment and obtains the corresponding hidden state weights and the degree of influence on the tailings dam deformation at the current moment. The final tailings dam deformation prediction result is obtained. The superposition and summation equation is shown in Equation (13). \[y=\sum_{i=1}^{N+1}y_{i} \tag{13}\]  
In the equation, \(N\) denotes the number of IMF subsequences; \(y\) denotes the final prediction of the displacement sequence; and \(y_{i}\) denotes the prediction result of the displacement component sequence. Figure 1: EEMD-DA-LSTM dam break risk prediction. (5) Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are selected as the evaluation metrics to assess the prediction accuracy of the EEMD-DA-LSTM model.