# Random Forest-Based Stability Prediction Modeling of Closed Wall for Goaf
## 7 Conclusions  
In this study, the overall process of predicting the impact of shock waves on closed walls using the RF algorithm involves several of the following key steps: data preparation, model selection, hyperparameter optimization, model training, cross-validation, and performance evaluation. A total of 167 data samples, each containing three predetermined key input parameters, were collected to train and validate the RF machine learning model. Based on the introduction of four performance evaluation metrics, namely, the accuracy, precision, recall, and F1-score, within the experimental context of this study, we drew the following conclusions:  
1. The analysis of the machine learning-based closed wall stability prediction model reveals that the random forest model demonstrates excellent predictive performance, achieving an accuracy of 94.6%. Following hyperparameter optimization through a grid search, the performance of the optimized random forest model is further enhanced, reaching an impressive accuracy of 95.9%. Compared to other algorithms, the method proposed in this paper does not exhibit a decrease in the prediction performance. Compared to the numerical model of [PERSON] et al. [19], the model presented in this paper improves the computational efficiency by approximately 40%. 2. Based on the model established in this study, the three parameters obtained from the experiments (the shock wave velocity, compressive strength, and shear strength) can effectively predict the stability of the closed walls in the project. To verify the reliability of the model, the machine learning prediction model was tested against data \begin{table}
\begin{tabular}{c c c c} \hline
**Goaf** & **Shock Wave (m/s)** & **Compressive Strength (MPa)** & **Shear Strength (MPa)** \\ \hline
1880-1 & 62.95 & 7.7 & 155.83 \\
1850-1 & 64.92 & 8.1 & 153.59 \\
1830-1 & 130.49 & 10.3 & 179.02 \\
1800-1 & 212.87 & 13.1 & 202.68 \\
1780-1 & 270.34 & 18.9 & 237.99 \\ \hline \end{tabular}
\end{table} 
Table 6: Sample data of the goat in the Duimenshan Mine. \begin{table}
\begin{tabular}{c c c} \hline
**Goaf** & **Prediction Results** & **Actual Results** \\ \hline
1880-1 & No damage & No damage \\
1850-1 & No damage & No damage \\
1830-1 & No damage & No damage \\
1800-1 & Minor damage & Minor damage \\
1780-1 & Serious damage & Serious damage \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of the predicted results with the actual results. from 74 engineering examples and the Duimenshan mining area. The results indicate that the predicted stabilities of the closed walls are basically in line with the actual conditions, confirming that the RF prediction model exhibits superior performance and accuracy. This high-performance prediction model provides a reliable and efficient tool for accurately assessing the impact of shock waves on closed walls, which is of great significance for theoretical research in the field of engineering geology. It fills a gap in the existing research to some extent by offering a more accurate and efficient prediction method for this specific engineering problem. This practical application not only validates the effectiveness of the model but also provides valuable guidance for real-world engineering projects, such as mine construction and underground engineering, where the stability of closed walls is crucial for ensuring safety and operational efficiency. Investigation, Y.Y., L.G. and Y.Z. ; methodology, K.H. and Y.Y. ; software, Y.Y. and H.S. ; conceptualization, K.H. ; resources, K.H. ; writing--original draft preparation, Y.Y. ; writing--review and editing, K.H., H.S. and L.G. ; supervision, K.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Author [PERSON] was employed by the company Yunnan Gold Mining Group Co., Ltd.