# Research on Hard Rock Pillar Stability Prediction Based on SABO-LSSVM Model
## 4 Results and Discussion
### Models Performance Comparison
In these two parameter combinations, the performance of the SABO optimization was comparable to that of PSO and GWO, but in M-5, the latter had a slight advantage, indicating that the two feature removals diminished the predictive performance of the optimized models, with the SABO-LSSVM being the most affected. The cumulative distribution graph provides an overall view of the distribution of prediction results for each model. To further compare quantitatively, line graphs of A10, A50, and A90 were plotted to assess the accuracy performance of the prediction results in different ranges, the results are shown in Figure 10. Taking A10 as an example, the \(y\)-axis represents the upper limit of accuracy when the cumulative distribution value of the prediction results of the model reached 10%, indicating that 10% of the prediction results in the sample were lower than or equal to this value. The A10 comparison graph reflects the upper limit of accuracy for each model when they performed poorly. A lower value indicates that the model was more likely to incorrectly predict pillar samples in the test set. From the graph, it can be seen that the SABO-LSSVM model had the best A10 value in the M-1 and M-2 combinations and ranked high in the other three combinations. The prediction results of the PSO, GWO, and WOA optimization models fluctuated between 70% and 78% when performing poorly, whereas the KOA and GA optimization models performed poorly, mostly below 70%. Figure 10: Equivalent accuracy of prediction results for different optimization models under each parameter combination (%). (**a**) Accuracy at a cumulative distribution of 10% (A10). (**b**) Accuracy at a cumulative distribution of 50% (A50). (**c**) Accuracy at a cumulative distribution of 90% (A90). The A50 graph shows that the SABO-LSSVM model achieved the highest value when using the M-3 combination dataset, indicating that at least half of the prediction results were below 84.85%. The best A50 values for the PSO-LSSVM and GWO-LSSVM models were 83.45% and 82.75%, respectively, indicating that, under the same number of runs, the SABO-optimized LSSVM model slightly outperformed the latter and other optimization models in terms of half of the predicted samples. The A90 graph reflects the ability of different models to achieve good prediction results. From the graph, it can be seen that the SABO-LSSVM model had an A90 value below 92% for the top 90% of the sample prediction accuracy, while the A90 values for the other models were below 90%, indicating a higher likelihood of higher accuracy for the SABO-optimized model compared to the other models. The WOA and GA optimization models performed poorly in terms of A90, with a maximum value of only 82%, indicating that these two optimization models had poor effects on improving the original model. In summary, the SABO-LSSVM model had the best accuracy in identifying pillar states, followed by the PSO-LSSVM and GWO-LSSVM models. The KOA-, WOA-, and GA-optimized models performed poorly. Among the five parameter combinations used in the SABO-LSSVM model, the best was the M-3 combination, which consisted of features W, H, K, UCS, and \(\sigma\).