Prediction of coal oxidation temperature rise in goaf based on grid optimization double-layer random forest
In order to predict the temperature of coal oxidation temperature rise in goaf,a long-term observation experiment of goaf gas and temperature was carried out on the 16402 fully mechanized caving face of a coal mine in Inner Mongolia to col-lect accurate gas and temperature data during the process of coal oxidation heating in goaf.A method for predicting the coal oxidation temperature rise in goaf based on the grid optimization double-layer random forest(WG-DRF)was proposed.The prediction model was constructed by this method and compared with the prediction results of traditional random forest,BP neural network and support vector regression model.The results show that the mean absolute error MAE,mean square error MSE and coefficient of determination R2 of WG-DRF model are 1.725,6.158 and 0.903,respectively,which are better than the other models.The WG-DRF method is tested by changing the data set,and it verified that the double-layer random forest model has strong generalization.The research results can provide reference for the temperature prediction of coal oxidation temperature rise in goaf.
goafcoal oxidation temperature risetemperature predictiongrid optimization double-layer random forest
万方数据
维普
