Coal and Gas Outburst Prediction Based on DBSCAN-IHHO-SVM Model
ZHENG Xiaoliang
WANG Qi
LAI Wenhao
ZHANG He
ZHANG Yuting
Abstract：The complexity of coal and gas outburst accidents and the low prediction accuracy caused by the difficulty of data acquisition were addressed by proposing the density-based spatial clustering of applications with noise-improved Harris hawks optimization-support vector machine(DBSCAN-IHHO-SVM)warning model.Firstly,gas content,gas pressure,coal seam porosity,and the coal seam robustness coefficient were selected as predictors,and missing values in the data were processed by mean filling.The amount of outburst data was expanded using a generative adversarial network(GAN).Secondly,DBSCAN was employed to identify potentially hazardous data from non-outburst data,which were then treated as new outburst data.Finally,the parameters of the SVM model adjusted by IHHO were introduced,and the processed data were fed into the IHHO-SVM model for predictive analysis.Compared with the original SVM model,the results showed that the overall prediction accuracy and hazardous data identification rate of DBSCAN-IHHO-SVM model were improved by 5.87%and 38.46%,respectively.When faced with limited outburst data samples,DBSCAN-IHHO-SVM model effectively mined the potential information of non-outburst data,achieving accurate early warning and offering new insights for research in this field.
Keywords：coal and gas outburstpredictiondangerous data identificationdata expansionIHHOSVM
Publication Date：2025-03-19
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 53-59 )