为了提高煤与瓦斯突出预测精度,选取瓦斯含量、瓦斯压力、瓦斯放散初速度等11个因素作为判别指标,将煤与瓦斯突出强度分为无突出、小型突出、中型突出、大型突出4个等级.利用贵州黔西北煤矿资料中的28组数据作为训练学习样本,建立了煤与瓦斯突出危险等级预测的PCA-Fisher判别分析模型,再利用资料中其余6组数据作为预测样本,对该模型进行检验和应用,并与BP神经网络模型和Fisher判别模型的判别结果进行比较.结果表明:PCA-Fisher判别模型具有更高的准确性和可靠性,可以对煤与瓦斯突出危险等级进行有效预测.%In order to improve the accuracy of coal and gas outburst prediction.11 factors, including gas content, gas pressure and initial velocity of gas, were selected as the discriminant indicators. The outburst intensity of coal and gas was divided into four grades: no outburst, small outburst, medium outburst and large outburst.28 sets of data of Qianxibei Coal Mine in Guizhou were used as training samples to establish a PCA-Fisher discriminant analysis model for risk level prediction of coal and gas outburst. Using the remaining 6 sets of data as a prediction sample to test and apply the model, the discriminant result was compared with that of BP neural network model and Fisher discriminant model. The results showed that PCA-Fisher discriminant model has higher accuracy and reliability, it can predict the risk level of coal and gas outburst effectively.
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