# Predicting the Mine Friction Coefficient Using the GSCV-RF Hybrid Approach
## 1 Introduction  
The coefficient of frictional resistance (\(\alpha\)) is an essential parameter for the calculating mine ventilation resistance, solving the ventilation network and optimizing the ventilation systems. The main method of obtaining this parameter is field measurement, but this method incurs a heavy workload that is also detailed and complicated and is easily affected by the operator, the equipment or the measurement method, leading to measurement result errors [1; 2; 3; 4]. In addition, with the advancement of ore body mining, the mining sites gradually become deeper, so that it is impossible to carry out survey work on the tunnels that are in the planning stage and not constructed, something which can also result in missing data. All these problems may affect the study of ventilation systems and reduce the safety and reliability of the system. Therefore, obtaining \(\alpha\) more quickly, accurately, and easily is a valuable research objective within ventilation system studies. To solve this problem, some scientific researchers began with data mining. [PERSON] [3] collected all the historical \(\alpha\), constructed an \(\alpha\) database, and matched the satisfied \(\alpha\) through a fuzzy query for the ventilation system design of roadways without resistance measurement work. [PERSON] et al. [5] introduced more detailed measurement indicators during the construction of their \(\alpha\) database, which can match the corresponding \(\alpha\) more accurately and precisely with the roadway and further improve the security of the ventilation system. However, the data mining method requires a large amount of detailed measurement data with rich measurement indicators in order to ensure the accuracy of the matched data. Therefore, collecting enough data for this method is a great challenge. Considering the data mining defects, scientific researchers have adopted machine learning, as it has a lower cost and requires less time to solve the detailed measurement problems and obtain reliable data quickly and easily [6]. [PERSON] et al. [7] started with the use of a back-propagation (BP) neural network to predict the \(\alpha\) of log-supported roadways, which provided a new method for obtaining an accurate and reliable \(\alpha\). [PERSON] [8] then followed the BP neural network. They started with a type of roadway support mode and constructed an \(\alpha\) prediction model for a variety of roadway support modes. This ensured that the \(\alpha\) prediction of the BP neural network was no longer limited to a certain type of roadway. [PERSON] [9] introduced the parameter of the cross-section shape of a roadway, optimizing the \(\alpha\) prediction model through the BP neural network and making more accurate \(\alpha\) predictions through the model. Most machine learning models used for predicting \(\alpha\) have been developed based on BP neural networks, but BP neural networks have disadvantages. These include the tendency of falling into a local minimum value, which leads to training failure and overfitting. Therefore, it is necessary to spend time adjusting the prediction model so as to ensure the prediction result accuracy [10; 11]. In addition to BP neural networks, there are many other machine learning methods with different characteristics. [PERSON] [12] proposed the random forest (RF) algorithm, which is superior in handling regression problems through the examination of detailed examples. The algorithm has the advantages of requiring fewer tuning parameters, having a higher training efficiency and requiring less overfitting than the BP neural network [13; 14]. For the prediction of the \(\alpha\) regression problem, the RF is also a solution method. [PERSON] et al. [15] followed the variety roadway prediction indicator system of [8] and constructed a variety of RF prediction models of \(\alpha\) and achieved better prediction results. However, there is still room for improvement.