基于主成分回归分析的瓦斯含量预测

Prediction of Coal Seam Gas Content Based on Principal Component Regression Analysis

为了增加多元回归模型预测的精度,将主成分分析与多元回归分析相结合提出了PCA-MRA模型,并将该模型用于实际瓦斯含量预测。结果表明,PCA-MRA模型消除了输入变量之间的相关性,减少了输入变量值个数,提高了预测精度,便于实际推广和应用,为瓦斯含量预测提供一种新的途径。

In order to predict coal seam gas content, a principal component analysis-multivariate regression analysis（PCA-MRA） mode is es- tablished, combined with the measured data. The results show that, coal seam gas content can be predicted effectively, avoiding complicating derivation and ealeulation. Compared with existing prediction methods, an intuitive and precise result is gotten,with computation time reduced significantly, exceedingly convenient for popularization and application. A novel approach for prediction of eal seam gas is provided.