Regional prediction and prevention analysis of rockburst hazard based on the Gaussian process for binary classification - Lan, Tianwei; Zhang, Zhijia; Sun, Jiawei; Zhao, Wenqi; Zhang, Mancang; Jia, Weidong; Liu, Mingwei; Guo, Xutao (Frontiers in Earth Science, 2022)
Rockburst is a complex dynamic disaster in coal mining and affected by many factors. To accurately predict the rockburst hazard among complex influencing factors, a prediction model of rockburst hazard based on Gaussian Process for Binary Classification(GPC) was proposed after the identification of the intrinsic relationship between multiple factors of coal mines and rockburst. Through computerized machine learning and integrated intelligent analysis the non-linear mapping of rockburst hazard and its influencing factors were established. The multi-factor pattern recognition model was constructed by using the artificial intelligence. The prediction criteria of rockburst hazard probability and the hazard probability value of the prediction area unit were determined by applying neural network and fuzzy inference methods.. Besides, the rockburst hazardous zone was classified and the corresponding technical scheme of the prevention was put forward. The validity and feasibility of the regional prediction of rockburst hazard based on GPC was verified in the engineering practice. This method is highly targeted and can improve the accuracy and precision of rockburst prediction, contributing to the safe and efficient production of coal mines.