Predicting the Mine Friction Coefficient Using the GSCV-RF Hybrid Approach

The safety and reliability of a ventilation system relies on an accurate friction resistance coefficient (α), but obtaining α requires a great deal of tedious measurement work in order to determine the result, and many erroneous data are obtained. Therefore, it is vital that α be obtained quickly and accurately for the ventilation system design. In this study, a passive and active support indicator system was constructed for the prediction of α. An RF model, GSCV-RF model and BP model were constructed using the RF algorithm, GSCV algorithm and BP neural network, respectively, for α prediction. In the GSCV-RF and BP models, 160 samples complied with the prediction indicator system and were used to construct a prediction dataset and, this dataset was divided into a training set and a test set. The prediction results were based on the quantitative evaluation models of MAE, RMSE and R2. The results show that, among the three models, the GSCV-RF model’s prediction result for α was the best, the RF model performed well and the BP model performed worst. In the prediction for all the datasets obtained by GSCV-RF model, all the values of MAE and RMSE were less than 0.5, the values of R2 were more than 0.85 and the value of R2 of the passive and active support test sets were 0.8845 and 0.9294, respectively. This proved that the GSCV-RF model can offer a more accurate α and aid in the reasonable design and the safe operation of a ventilation system.