## 5 Results and Discussion
### Result Comparison and Discussion  
The predicted results do not directly provide the uniaxial compressive strength for various rock types; therefore, it is necessary to reclassify them to determine the uniaxial compressive strength of different rock types. The classification of the predicted uniaxial compressive strength values is presented in Table 5. Based on a comparison between the predicted results and the actual drilling data, the five drilling holes were designated as Drilling Hole 1, Drilling Hole 2, Drilling Hole 3, Drilling Hole 4, and Drilling Hole 5. The depths of these drilling holes ranged from 7.9 m to 8.1 m. Figure 9 illustrates the comparison between the actual values and the predicted results from the three types of models. The SVM predictions for drilling holes 1-5 are accurate, except for a coal prediction error of 0.52 m in drilling hole 5. The prediction results for the three types of lithology using the linear model from drill hole 1 are all incorrect, yielding a total accuracy of 83%, while all tree model predictions are correct. In borehole 2, there are errors of 0.5 m for siltstone and 0.51 m for medium-grained sandstone in the tree model, whereas the linear model exhibits errors in predicting the three types of lithology, with accuracy rates of 82% and 76%, respectively. Most predictions from the pore model for drill hole 3 classify siltstone and medium-grained sandstone as different types. The tree model predicts 0.83 m of siltstone as coal, while the linear model predicts 0.47 m as medium-grained sandstone. In drilling hole 4, the tree model only incorrectly predicted 0.64 m of siltstone and medium sandstone, achieving an accuracy of 95%, while the linear model made multiple errors, resulting in an accuracy of only 58%. For drilling hole 5, both model types correctly identified medium sandstone, but errors were present in the predictions for coal and siltstone, with accuracy rates of 78% and 83%, respectively. The prediction accuracies of the SVM model for three types of lithology--coal, siltstone, and medium sandstone--are 0.94, 1.00, and 1.00, respectively. The prediction accuracies of the tree and linear models are illustrated in Figure 10. The average accuracies of the tree model for the three types of lithology are 0.86, 0.78, and 0.94. In contrast, the average accuracies of the linear model for the same lithologies are 0.50, 0.80, and 0.81. Among the three models, the highest prediction accuracy is observed for medium-grained sandstone. The SVM and linear models demonstrate greater accuracy in predicting sandstone compared to coal, while the tree model exhibits a higher prediction rate for coal. Although the SVM model performs well on the whole, there are still some prediction errors in individual samples. Through the analysis of these error samples, it is found that they are mainly concentrated in the lithological transition area, such as the junction of coal and siltstone. This may be due to the complex changes of rock mechanical properties in these areas, and the characteristics of signals while drilling are not obvious enough, resulting in the deviation of the model in prediction. In future research, we will further optimize feature selection and model structure to improve the prediction accuracy of the model in these complex regions. The coal and rock mass strength prediction model based on AdaBoost SVM proposed in this study has broad application prospects in coal mines. Through the real-time monitoring of drilling signals and the prediction of coal and rock mass strength, it can provide a scientific basis for the optimization of drilling parameters, the classification of roadway \begin{table}
\begin{tabular}{c c c} \hline \hline
**Predicted Value Range/MPa** & **Classification Value/MPa** & **Lithology** \\ \hline
0–15 & 9.7 & Coal \\
16–29 & 22.1 & Silstone \\
30–43 & 38.6 & Medium-grained sandstone \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification of predicted values for uniaxial compressive strength.