Abstract
Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal-gas outburst prediction in deep coal mines, this study proposes a deep coal-gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal-gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal-gas outburst risks.