# Coal and Gas Outburst Risk Prediction and Management Based on WOA-ELM
## 5 Results and Discussion
### WOA-ELM Model Prediction Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Actual Value** & \begin{tabular}{c} **WOA-ELM** \\ **Predicted Values** \\ \end{tabular} & \begin{tabular}{c} **ELM Predicted** \\ **Values** \\ \end{tabular} & \
\begin{tabular}{c} **GA-ELM** \\ **Predicted Values** \\ \end{tabular} &
\begin{tabular}{c} **CS-ELM** \\ **Predicted Values** \\ \end{tabular} \\ \hline
6 & 1 & 1.025 & 2 & 1.298 & 1.5 \\
11 & 1 & 1.031 & 2 & 1.5 & 1.5 \\
16 & 1 & 1.094 & 2 & 1.73 & 1.33 \\
21 & 1 & 1.119 & 2 & 1.3 & 1.233 \\
49 & 3 & 3.25 & 2 & 2.87 & 3.24 \\
54 & 3 & 3 & 2.94 & 3.09 & 3.29 \\
57 & 3 & 3 & 2.97 & 3 & 3 \\
67 & 2 & 2 & 2.4 & 2.2 & 2 \\
70 & 1 & 1 & 1.5 & 1.3 & 1.19 \\
80 & 2 & 2 & 2.09 & 2 & 2 \\
88 & 3 & 3.4 & 3.45 & 3.35 & 2.7 \\
96 & 2 & 2 & 2.2 & 1.7 & 1.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Sample deviations of some gas outburst hazard predictions. To further verify the optimized performance of the ELM model, six benchmark test functions are selected for testing, as shown in Table 11, where \(f_{1}\sim f_{5}\) are single-peaked functions, and \(f_{6}\) is a multi-peaked function. The roles of single-peaked and multi-peaked processes are different. The single-peaked function can verify the development ability of the algorithm, while the multi-peaked function can verify the global exploration ability of the algorithm. The population size of WOA is assumed to be 50, with 200 iterations. Observe the convergence of the two algorithms under each benchmark function, as shown in Figure 6. When dealing with the F1 function, the ELM algorithm has insufficient convergence ability and poor effect. The GA-ELM and the CS-ELM algorithms have better convergence ability than the ELM algorithm, while the WOA-ELM algorithm has the best convergence ability. The WOA-ELM algorithm falls into the local optimum in the middle of the iteration. However, it breaks out of the limitation of local optimum in the later iteration, and the later iteration effect is good, as shown in Figure 6a. When processing the F2 function, the curves of the four algorithms tend to be flat in the early stage of iteration, and the curves begin to show a significant downward trend after 95 iterations. Although the WOA-ELM algorithm does not reach the whole field optimal, the convergence effect of the WOA-ELM algorithm is better than that of the ELM, the GA-ELM, and the CS-ELM algorithms, as shown in Figure 6b. When dealing with F3, the ELM algorithm falls into the local optimum at the beginning of the iteration and shows premature convergence. It has not been free from the constraint of local extreme value, and the effect is poor. The convergence effect of the WOA-ELM algorithm is better than that of the ELM algorithm. Although it does not reach the whole field optimum, it is higher than the ELM algorithm in terms of convergence precision. The convergence effect of the GA-ELM and the CS-ELM algorithms is also better than that of the ELM algorithm, but the convergence speed is slower. In terms of the overall effect, the convergence effect of the WOA-ELM algorithm is the best among the four algorithms, as shown in Figure 6c. When dealing with the F4 function, the iterative curve of the ELM algorithm tends to be flat. The WOA-ELM algorithm converges to the global optimum when it iterates 100 times, achieving good results. The GA-ELM converges