Quantitative Analysis of Prediction Indicators for Coal and Gas Outburst Risk

This study assesses the risk of coal and gas outburst using the theory of rock engineering system, selects seven prediction indices that are significantly related to outburst, and improves on the Back Propagation (BP) neural network technique by applying the Sparrow Search Algorithm (SSA). The improved SSA-BP network is expected to yield discoveries with higher calculation accuracy. Simultaneously, quantitative and qualitative indicators are obtained, and a coal and gas outburst prediction model is constructed based on the cloud drop chart's subordination degree of outburst indicators. The created cloud model predicts the hazard of coal and gas outburst in analyzed coal seams. Eight of the previous nine measurement locations are at risk of medium coal and gas outburst, while only one is at risk of a super big coal and gas outburst.