Application of partial leastsquares regression in the forecast of ground subsidence
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Abstract:
Taking into account many influence factors of ground subsidence induced by underground exploitation,based on partial leastsquares 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 leastsquares multinomial regression is a better and feasible nonlinear method.
