# Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model
## 4 Empirical Analysis
### Risk Factor Correlation Analysis  
Tailings pond dam failure accidents are affected by various risk factors, such as rainfall, dip line, reservoir water level, and dry beach length. The safety monitoring data of tailings pond WKB-1 were used as sample data to analyse the trends of tailings pond dam failure risk factors. Tailings pond WKB-1 is equipped with the J1 measuring point, J2 measuring point, and J3 measuring point, which are three displacement monitoring points. Scatterplots were used to show the trends of tailings pond dam failure risk factors in the same time period and to analyse the trends of the displacement of the tailings pond at measurement point J1 with the displacement of measurement point J2, displacement of measurement point J3, horizontal displacement in the y-direction of measurement point J1, settlement displacement in the z-direction of measurement point J1, rainfall, height of the infiltration line, height of the reservoir water level, and length of the dry beach. The changes in risk factors for tailings pond failure in a certain time period are shown in Figure 2.  
Figure 2: Scatterplot of changes in dam failure risk factors. Pearson's correlation coefficients were used to explore the correlation between dam failure risk factors, to quantify the influence of each dam failure factor on the displacement change in the J1 measurement point of tailings pond WKB-1, and to screen the main characteristic factors of tailings dam deformation. The equation for calculating the Pearson's correlation coefficient is shown in Equation (17). \[r=\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{ \sum\limits_{i=1}^{n}{(x_{i}-\overline{x})^{2}}\sqrt{\sum\limits_{i=1}^{n}{(y_ {i}-\overline{y})^{2}}}}}\in[-1,1] \tag{17}\]  
In the equation, \(r\) denotes the correlation coefficient between two characteristic factors; \(n\) indicates the number of samples; \(x_{i}\) and \(y_{i}\) denote the ith sample value of the two eigenfactors, respectively; and \(\overline{x}\) and \(\overline{y}\) denote the sample means of the two eigenfactors, respectively. Generally when \(|r|>0.4\), the two sets of samples can be considered to be linearly correlated. The three-dimensional displacements of the J1, J2, and J3 measuring points of tailings pond WKB-1 are denoted as \(D_{1}\), \(D_{2}\), and \(D_{3}\), respectively; the horizontal displacements in the \(x\)-direction and \(y\)-direction of the J1 measuring point are denoted as \(X_{\text{D}}\) and \(Y_{\text{D}}\), respectively; and the settlement displacements in the z-direction are denoted as \(Z_{\text{D}}\). The internal displacement, reservoir level height, rainfall, dry beach length, and dip line height are denoted as \(I_{\text{D}}\), \(K\), \(F\), \(B\), and \(L\), respectively. The Pearson's correlation coefficients between \(D_{1}\) and \(D_{2}\), \(D_{3}\), \(X_{\text{D}}\), \(Y_{\text{D}}\), and \(L\) were calculated and the results are shown in Table 1 and Figure 3.  
From the results of the \(D_{1}\) correlation coefficient calculations in Table 1 and Figure 3, it can be seen that the correlation coefficients between the displacement of the J1 measuring point of tailings pond WKB-1 and the displacement of the J2 measuring point, the displacement of the J3 measuring point, the horizontal displacement in the \(x\)-direction of the J1 measuring point, and the height of the dip line are more than 0.75, which has a strong positive correlation, positive correlation with the change in the reservoir water level height, and negative correlation with the horizontal displacement in the \(y\)-direction of the J1 measuring point and the vertical displacement.