# Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors
## 5 Prediction Results and Analysis
### Analysis
As seen from Table 4, dimensionless methods have the greatest impact on the SCG-BP model and the least impact on the GDM-BP model. The results show that the GDM-BP model has the minimum requirements for the dimensionless method, and the prediction accuracy differs little when different dimensionless methods are adopted and vice versa for the SCG-BP model. Similarly, the extreme difference values of \(C\) for different prediction models under the same dimensionless method can be calculated, as shown in Table 5. \begin{table}
\begin{tabular}{c c} \hline
**Prediction Model** & **Extreme Difference** \\ \hline Standard BP model & 21 \\ GDM–BP model & 7 \\ BFG–BP model & 19 \\ SCG–BP model & 67 \\ LM–BP model & 49 \\ \hline \end{tabular}
\end{table} 
Table 4: Extreme difference values of \(C\) for different dimensionless methods under the same model. \begin{table}
\begin{tabular}{c c c c c} \hline
**Prediction Model** & \begin{tabular}{c} **Unified Extreme Value** \\ **Processing Method** \\ \end{tabular} & \begin{tabular}{c} **Differentiated** \\ **Extreme Value** \\ **Processing Method** \\ \end{tabular} & \
\begin{tabular}{c} **Normalized** \\ **Processing Method** \\ \end{tabular} &
\begin{tabular}{c} **Data Averaging** \\ **Processing Method** \\ \end{tabular} \\ \hline Standard BP model & 145 & 124 & 134 & 133 \\ GDM–BP model & 134 & 132 & 127 & 133 \\ BFG–BP model & 167 & 156 & 152 & 171 \\ SCG–BP model & 185 & 166 & 118 & 166 \\ LM–BP model & 194 & 185 & 145 & 187 \\ \hline \end{tabular}
\end{table} 
Table 3: Comprehensive, accurate values \(C\) of different models. \begin{table}
\begin{tabular}{c c} \hline
**Dimensionless Method** & **Extreme Difference** \\ \hline Unified extreme value processing & 60 \\ Differentiated extreme value processing & 51 \\ Normalized processing & 34 \\ Data averaging processing & 54 \\ \hline \end{tabular}
\end{table} 
Table 5: Extreme difference values of \(C\) for different prediction models under the same dimensionless method. According to Tables 4 and 5, compared with the impact of the dimensionless method on the prediction model, the selection of BP neural network models has a more significant impact on the prediction results. From the above analysis, it is clear that both the dimensionless method and the model training method are important factors affecting the prediction effect of the model. Therefore, the model prediction accuracy \(P_{\text{a}}\) and the prediction stability \(P_{\text{s}}\) are considered in this paper for model optimization. \(P_{\text{a}}\) is measured by the comprehensive, accurate value \(C\) and \(A_{\text{max}}\) (the prediction accuracy under the maximum sample size), where the \(C\) reflects the comprehensive prediction performance of the model under different sample sizes, the \(A_{\text{max}}\) reflects the prediction effect when the size of samples is the largest, and the combination of the two can ensure the prediction accuracy effectiveness. The prediction stability \(P_{\text{s}}\) is measured by the \(C\)'s extreme difference. For model preference, prediction accuracy \(P_{\text{a}}\) is considered first, and prediction stability \(P_{\text{s}}\) can be used as an auxiliary discriminant factor. If the model prediction accuracy varies greatly, the influence of stability can be ignored. When the difference between the two models' \(C\) values is less than or equal to 5, the model with better prediction stability is preferred in this paper. Based on the above analysis, the predicted effect of the LM-BP neural network based on the unified extreme value processing method is best among the sixty prediction models, and its \(A_{\text{max}}\) (100) = 97.0% and \(C\) = 194, both of which are maximum values. Therefore, the LM-BP model is the best optimal model in this paper.