Risk Warning Method for Major Mining Disasters Based on BES-RBF Neural Network
JIANG Jincheng
LIU Yexian
CAO Huaixuan
ZHANG Hao
FAN Peng
Abstract：To address the issues of delayed emergency response and increased risks and losses due to untimely identification of potential safety hazards in mines,this paper proposes a major mine disaster risk warning method based on a BES-RBF neural network.Kernel Principal Component Analysis(KPCA)is employed to extract features associated with mine disasters from complex mine data.The Radial Basis Function(RBF)neural network is adopted as the warning model for major mine disasters.The Bald Eagle Search(BES)algorithm is utilized to optimize the network parameters,thereby establishing an optimal network model.The extracted features are then used as inputs to the trained network,which outputs the relative risk value of mine disasters.This value is classified into different risk levels to achieve major mine disaster risk warning.Experimental results demonstrate that the proposed method achieves a high KS value while maintaining low CPU and memory occupancy.
Keywords：Major mine disastersKernel principal component analysisBald Eagle Search AlgorithmRadial basis function neural networkRisk warning
Publication Date：2025-12-30
Online Publishing Date：2025-12-15(First online date of this platform, not the publication date of the document)
Pages：7( 180-186 )