Coal mine gas emission prediction method based on random forest regressio

In order to improve the prediction accuracy and efficiency of coal mine gas emission, a coal mine gas emission prediction method based on random forest regression is proposed. The bootstrap self-service resampling technology is used to collect training sample data and construct a random forest regression model. The mean value of the decision tree output value is taken as the prediction result of coal mine gas emission and the out-of-bag data is used to evaluate the prediction performance of the regression model. The optimal hyperparameters of the random forest regression model are determined by calculating the mean of squared residuals and goodness of fit of the out-of-bag data. The increase in the mean of squared residuals of the out-of-bag data is used to characterize the importance of the characteristic variables. All the characteristic variables of coal mine gas emission are replaced by some characteristic variables with cumulative influence weight of 90%. And eight characteristic variables with high importance are selected as input variables of the model, including coal mining height, coal thickness, coal seam gas content, recovery rate, burial depth, daily progress, mining intensity and adjacent layers spacing. The test results show that the random forest regression model with all characteristic variables and some characteristic variables has good prediction performance. After selecting characteristic variables, the average absolute error of the model decreases from 022 m3/min to 021 m3/min, and the average relative error decreases from 355% to 347%. The random forest regression model based on characteristic variable selection reduces the dimensionality of the characteristic variables of the prediction model, reduces the original data acquisition work, and improves the prediction efficiency under the premise of ensuring better prediction performance.