## 5 Experimental Results  
In this study, a BP neural network and PSO-BP neural network were used as comparisons against the proposed ISSA-BP model, which was used to assess the model's prediction accuracy and performance. Fitness is an index that measures the pros and cons of individuals in the population. The fitness convergence curve can reflect the algorithm's prediction performance and convergence efficiency, and the convergence curve of the ISSA-BP after the model's run is shown in Figure 8.  
Figure 8: Convergence graph of the ISSA–BP adaptation. In Figure 8, the adaptation curve gradually flattens out and steadies into a development condition, which reduced the instability of the model in this paper. Thanks to its two improvements (i.e., use of adaptive weights and backward learning strategy), the ISSA-BP model showed good performance and great improvement in the performance of the BPNN. In this section, other neural networks are compared with the ISSA-BP proposed in this paper, and we chose the PSO-BP and BP models as the benchmark models and plotted the predicted results against the true values, as shown in Figure 9.  
Figure 9: (**a**) Results of the ISSA–BP on FOS; (**b**) Results of the PSO–BP on FOS; (**c**) Results of the BP on FOS. Figure 9 shows that compared with the other prediction models, the FOS predicted by the ISSA-BP model was very consistent with the actual FOS. The absolute error distribution was approximately 0.02. The operating results of the PSO-BP model were quite different from the actual value, and there was no tendency to fit the actual value, while for the BP neural network, the data offset was too large. When the error generated by the training samples was smaller than the real error or almost the same, it proves that the ISSA-BP model in this study had a strong generalization ability and the best prediction effect. The scatter plots for the ISSA-BP, PSO-BP, and BPNN model prediction results are shown in Figure 10.  
Figure 10: Comparison of scatter plots of the predicted and actual FOS values for the three models. (**a**) Prediction results for ISSA-BP; (**b**) Prediction results for PSO-BP; (**c**) Prediction results for BPNN. Figure 10 shows the regression coefficients \(\mathrm{R}^{2}\) for each model. The ISSA-BP model with an \(\mathrm{R}^{2}\) value of 0.99 had the best-fit line between the projected and actual safety coefficient values, and the correlation between the output data and the actual data was large. The \(\mathrm{R}^{2}\) value of the PSO-BP was 0.8197, the \(\mathrm{R}^{2}\) value of the BPNN was 0.6283, and the fitting effect needs to be improved. Meanwhile, it is not difficult to observe that the prediction results of the unimproved BPNN show that some data also performed well, although it can be seen from the figure that there were deviations in the data that were too large, which also reflects the network characteristic of a grid structure that is unstable, and it easily slips into the local minima, requiring further exploration and development. The FOS comparison results for the training set of the slope predicted by the ISSA-BP model is shown in Figure 11, and the anticipated values were almost identical to the actual values. According to the above figure, the FOS errors produced by most of the datasets were close to 0. Therefore, the average absolute error value was approximately 0.021541, which indicates that the ISSA-BP model works very well and confirms the high prediction accuracy of the ISSA-BP model. According to the prediction performance assessment indices of the neural network models in Table 2, the ISSA-BP model had the best prediction effect, with an average absolute percentage error of 1.64%, root mean square error of 0.0215, and coefficient of determination of 0.99. The average absolute error was 0.0215 and the average absolute error of the ISSA-BP was reduced by 78% compared with the BPNN.