Roof displacement prediction of coal roadways based on random forest algorithm and the applica-tion
The stability control of surrounding rocks in coal roadways is the key to ensure the safe and efficient mining of coal mine.Roof displacement is a crucial indicator to present wall rock stability.In this paper,machine learning method was introduced to predict the roof displacement of coal roadways in advance.Eight important influence indexes of roof displacement of coal roadways were defined,and the prediction database of roof dis-placement of coal roadway was established,from which the data were analyzed for correlation and importance.Based on RF,GA-SVM and GA-ANN,three roof displacement prediction models of coal roadways were estab-lished respectively,and RMSE,MAE and R2 were selected to evaluate the performance of the models.The results show that the RF model has the best performance,R2=0.909,RMSE=20.475,MAE=16.790,while the GA-ANN model has the worst performance.The ten-fold cross-validation method was used to verify the reliability of RF and GA-ANN models,and it is found that RF model has higher stability,with an average R2 of 0.891.When RF model is applied to No.2-1121 roadway of Ganhe Coal Mine,the absolute error between the predicted and the actual value is 19 mm,and the relative error is 11.18%.This study shows RF model can accurately and stably predict roadway roof displacement,which provide a new reference for roadway roof displacement.
random forestcoal mine roadwayroof displacementmachine learningprediction
NETL
NSTL
万方数据
