基于主成分-逐步回归分析法的瓦斯涌出量预测研究

Prediction of gas emission based on principal component-stepwise regression analysis

矿井进行瓦斯涌出量预测是煤矿安全生产十分重要的工作,鉴于主成分分析和逐步回归分析方法的优点,将两种方法相结合共同建立瓦斯涌出量回归预测模型。以峻德煤矿30号煤层为例,通过主成分分析得到了影响回采工作面瓦斯涌出量的四个主成分因素,再采用逐步线性回归分析法预测回采工作面瓦斯涌出量。结果表明:采用主成分-逐步回归分析法减少了回归分析所需要考虑的变量个数,预测结果具有较好的准确性,预测精度明显优于一元回归预测和多元回归预测,具有较好应用前景。

Mine gas emission prediction is very important to coal mine safety production,in view of the advantages of principal component analysis and stepwise regression analysis,we combine the two methods to establish a gas emission regression prediction model.Taking the No.30 coal seam of Junde Coal Mine as an example,four principal component factors affecting the gas emission in the mining face are obtained by principal component analysis,and the stepwise linear regression analysis is used to predict the gas emission in the mining face.The results show that the principal component-stepwise regression analysis reduces the number of variables that need to be considered in the regression analysis.The prediction has favorable accuracy,which is better than the one-way regression prediction and multiple regression prediction,thus it has a good application prospect.