偏最小二乘回归在地表沉陷预测中的应用

考虑地下开采引起的地表沉陷的众多影响因素,基于偏最小二乘二次多项式回归这一非线性方法,对地表沉陷的最大值进行了预测。以地表最大沉陷值为因变量,以采高、采深、煤层倾角、硬度系数等为自变量,得出了地表最大沉陷值的预测模型。结果发现,Press残差值随潜变量个数的增加而降低,由两者关系图可确定潜变量的个数为4对;采高的标准回归系数最大,说明4个影响因素中采高对地表沉陷值的影响最大;预测模型的决定系数为0.9157,预测值的误差率为±10.41%,表明用偏最小二乘二元多项式回归方法预测地表沉陷是可行的。

Application of partial least squares regression in the forecast of ground subsidence

Taking into account many influence factors of ground subsidence induced by underground exploitation,based on partial least squares multinomial regression,a forecast analysis on the maximum of ground subsidence is carried out.Taking height,depth,obliquity of coal clay and rigidity coefficient as independent variables,and maximum of ground subsidence as dependent variable,the forecast model of maximum of ground subsidence is obtained.It is found that,Press residual value decreases with the increase of number of latent variables,and the number of latent variables is four by Press residual value versus number of latent variables.The normal regression coefficient of height is the largest in the four influence factors,and this indicates that the influence of height is the largest on maximum of ground subsidence.The determination coefficient of forecast model obtained in this paper is 0.915 7,the error of forecast model is ±10.41%.The following conclusion can be drawn that the model based on partial least squares multinomial regression is a better and feasible non linear method.