# Predicting the Mine Friction Coefficient Using the GSCV-RF Hybrid Approach
## 4 Conclusions  
In this study, in order to solve problems such as the minute and complicated work and larger measurement errors in the \(\alpha\) prediction of mines, we utilized an RF algorithm to build a prediction model that yields accurate and reliable \(\alpha\) results. Because RF prediction results are more influenced by the super parameter, the GSCV algorithm was developed to optimize RF's hyperparameters, and the GSCV-RF model was constructed to predict \(\alpha\). In order to determine whether or not RF was superior in \(\alpha\) prediction, the authors constructed a BP model using a BP neural network, which is a common method of obtaining \(\alpha\), and used it to make \(\alpha\) predictions for the same datasets. The quantitative evaluation of each model's prediction results was illustrated by the values of \(MAE\), \(RMSE\) and \(R^{2}\), and a graphical representation of the relationship between the actual sample measurement value and the prediction value and errors was provided. Therefore, the conclusion of the paper is as follows:  
1. The paper began with the roadway support type, and after classifying the roadways as passive support or active support, the passive support \(\alpha\) prediction indicator system and the active support \(\alpha\) prediction indicator system were developed, respectively. The study demonstrated that the accuracy of these two support systems combined with machine learning, which can successfully predict \(\alpha\), is dependent on the algorithm employed. 2. The paper introduced the RF algorithm to solve the problem of \(\alpha\) determination. To avoid the super parameter's influence, the GSCV algorithm was also introduced, and the GSCV-RF prediction model was constructed to predict the passive support training sets. The results were \(MAE=0.0018\), \(RMSE=0.0025\) and \(R^{2}=0.9965\). In the prediction of the passive support test sets, the results were \(MAE=0.0112\), \(RMSE=0.0157\) and \(R^{2}=0.8845\). In the prediction of the active support training sets, the results were \(MAE=0.0014\), \(RMSE=0.0019\) and \(R^{2}=0.964\). In the prediction of the active support test sets, the results were \(MAE=0.0024\), \(RMSE=0.0028\) and \(R^{2}=0.9294\). The smaller \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Predictive Models**} & \(MAE\) & \(RMSE\) & \(R^{2}\) \\ \hline RF & Passive Support & 0.0019 & 0.0029 & 0.9952 \\ GSCV-RF & Training Set & 0.0018 & 0.0025 & 0.9965 \\ BP & & 0.0111 & 0.0136 & 0.8945 \\ RF & & 0.0112 & 0.0159 & 0.8814 \\ GSCV-RF & Passive Support & 0.0112 & 0.0157 & 0.8845 \\ BP & Test Set & 0.0127 & 0.0182 & 0.8455 \\ RF & Active Support & 0.0010 & 0.0015 & 0.9775 \\ GSCV-RF & Training Set & 0.0014 & 0.0019 & 0.9641 \\ BP & & 0.0038 & 0.0050 & 0.7533 \\ RF & & 0.0027 & 0.0031 & 0.9165 \\ GSCV-RF & Active Support & 0.0024 & 0.0028 & 0.9294 \\ BP & Test Set & 0.0041 & 0.0056 & 0.7235 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Quantitative evaluation of the model results. MAE_ and _RMSE_, as well as the larger \(R^{2}\), demonstrated that the GSCV-RF model can produce more accurate and reliable predictions of \(\alpha\). 3. After comparing and analyzing the three models, we concluded that the GSCV-RF model was superior in \(\alpha\) prediction, followed by the RF model and the BP model. The BP model's \(R^{2}\) was too low, proving that the GSCV-RF model was superior in \(\alpha\) prediction. Therefore, the GSCV-RF model is able to avoid the tedious and time-consuming work involved in filed measurements and to obtain accurate and reliable \(\alpha\) results. In the design of ventilation systems, the GSCV-RF model should be utilized to keep ventilation systems in a safe and reasonable state. Conceptualization, C.G. and X.W. ; methodology, C.G. and X.W. ; software, C.G. and D.H.; validation, C.G. ; data curation, H.L. and M.J.; writing--original draft preparation, C.G. ; writing--review and editing, C.G., X.W. and J.L. ; visualization, C.G. and Y.Z. ; funding acquisition, X.W. and J.L.