Journal
APPLIED SCIENCES-BASEL
Volume 13, Issue 22, Pages -Publisher
MDPI
DOI: 10.3390/app132212283
Keywords
directional splitting; damage region; coalbed methane mining; coalbed permeability enhancement
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The XGBoost-GR-stacking gas outburst early warning model proposed in this article demonstrates high accuracy and practical performance, making it suitable for gas outburst risk warning in mining safety. The model is based on gas outburst data from 26 mines and establishes a data generation model based on XGBoost. Grey correlation analysis is used to select the main control factor groups based on the sorting of correlation degrees. Comparing the actual and predicted values for each model, the stacking fusion model achieves the highest accuracy in gas outburst prediction and the best model fitting effect.
Featured Application The XGBoost-GR-stacking gas outburst early warning model established in this article demonstrates high accuracy and practical performance, making it suitable for gas outburst risk warning in mining safety.Abstract To improve the accuracy of gas outburst early warning, this paper proposes a gas outburst risk warning model based on XGBoost-GR-stacking. The statistic is based on gas outburst data from 26 mines and establishes a data generation model based on XGBoost. The obtained virtual datasets are analyzed through visualization analysis and ROC curve analysis with respect to the original data. If the augmented data has an ROC area under the curve of 1, it indicates good predictive performance of the augmented data. Grey correlation analysis is used to calculate the grey correlation degrees between each indicator and the gas emission. The indicator groups with correlation degrees greater than 0.670 are selected as the main control factor groups based on the sorting of correlation degrees. In this study, SVM, RF, XGBoost, and GBDT are selected as the original models for stacking. The original data and virtual data with correlation degrees greater than 0.670 are used as inputs for SVM, RF, XGBoost, GBDT, and stacking fusion models. The results show that the stacking fusion model has an MAE, MSE, and R2 of 0.031, 0.031, and 0.981. Comparing the actual and predicted values for each model, the stacking fusion model achieves the highest accuracy in gas outburst prediction and the best model fitting effect.
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