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Research on Coal and Gas Outburst Risk Warning Based on Multiple Algorithm Fusion

Authors :
Yanlei Guo
Haibin Liu
Xu Zhou
Jian Chen
Liwen Guo
Source :
Applied Sciences, Vol 13, Iss 22, p 12283 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

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.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
22
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.4a41c0f393334a54afb16a8580e6a2d4
Document Type :
article
Full Text :
https://doi.org/10.3390/app132212283