# Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model
## 4 Empirical Analysis
### EEMD-DA-LSTM Modelling  
Tailings dam failure is a strongly nonlinear dynamic process and the tailings dam failure risk prediction model was constructed based on ensemble empirical modal decomposition (EEMD) and long short-term memory neural network (LSTM) to predict tailings dam displacement changes. The model makes full use of EEMD's smoothing ability for nonlinear data and LSTM's long-term memory ability for nonlinear data. The basic prediction process of tailings pond dam failure risk based on EEMD-LSTM is shown below:  
(1) Displacement time series data smoothing: Based on the results of outlier detection and missing value processing of the displacement of the J1 measurement point of tailings pond WKB-1, the displacement time series data with strong nonlinear characteristics are decomposed into a number of relatively smooth components using the EEMD method, [_IMF\({}_{1}\)_, _IMF\({}_{2}\)_,..., _IMF\({}_{N}\)_, _Res_].  
(2) Feature downscaling: Based on the pre-processing results of the risk monitoring data, such as displacement, reservoir level, and dip line, of tailings pond WKB-1, Pearson correlation coefficients are used to screen the tailings dam deformation impact factor (\(F_{1}\), \(F_{2}\),..., \(F_{k}\)).  
(3) LSTM model prediction: Each component obtained from the EEMD decomposition is separately combined with the deformation impact factor as a model input, [_IMF\({}_{i}\)_, \(F_{1}\)_, \(F_{2}\)_,..., _F\({}_{k}\)_], the LSTM model is constructed separately, the parameters such as learning rate, iteration number, and training batch of the model are set, model training is carried out, the optimal network structure is sought, and the change in the displacement component is predicted.  
(4) Superposition reconstruction: the prediction results of the displacement components are superimposed and summed as the displacement sequence prediction results. The superposition and summing equation is shown in Equation (18).  
\[y=\sum_{i=1}^{N+1}y_{i} \tag{18}\]  
In the equation, \(N\) denotes the number of IMF subsequences; \(y\) denotes the prediction result of the displacement sequence; and \(y_{i}\) denotes the prediction result of the displacement component sequence.  
Tailings pond J1 point displacement monitoring data and deformation influencing factor data, such as reservoir water level, dip line, and horizontal displacement, of valley-type tailings pond WKB-1 were selected as samples and combined with the J1 point displacement component obtained from EEMD decomposition as well as the main influencing factors of deformation to predict the risk of tailings pond dam failure. The preprocessed dataset was divided into the 80% training set and 20% test set. The EEMD-DA-LSTM model had three hidden layers. Among them, the FA-LSTM layer had two hidden layers and the TA-LSTM layer had one hidden layer. The parameter settings of the EEMD-DA-LSTM model are  
Figure 5: Prediction results of EEMD-LSTM model.  
shown in Table 4 and the activation function of this model was the ReLU function during training. At this time, the EEMD-DA-LSTM prediction model has the best performance.