Leveraging Data Mining for Enhanced Coal and Gas Outburst Prediction in Mining Operations
Keywords:
mine safety, predictive modeling, risk assessmentAbstract
The safety of mine production is significantly jeopardized by coal and gas outburst accidents. In order to enhance the scientific precision of predicting coal and gas outburst risks, a system software (V1.2.0) was developed using the C/S architecture, Visual Basic development language, and SQL Server 2000 database. The statistical process control (SPC) method and logistic regression analyses were employed to evaluate and establish the critical value of outburst risk for individual indices, such as the S value of drill cuttings and the K1 value of the desorption index. Through a multivariate information coupling analysis, the interrelation of outburst warnings was explored, leading to the derivation of the outburst risk prediction equation. Subsequently, the SPC and logistic regression analysis methods were applied to typical mines. The results demonstrated the accurate determination of sensitivity values for each borehole depth using the SPC method, with a 94.7% accuracy rate achieved by the logistic regression method. These methodologies prove to be valuable for the timely detection of outburst hazards during mining operations.
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