Calculation of surface subsidence coefficient in mining areas using support vector machine regression

In order to investigate and construct an intelligent model for calculating surface subsidence coefficients in mining areas,a new method by combining particle swarm optimization algorithm(PSO) and support vector machine(SVM) regression method is presented.In this method,the PSO algorithm is used to optimize the parameters of SVM regression.An intelligent calculation model for surface subsidence coefficient using this hybrid PSO-SVM algorithm is constructed based on the analysis of impact factors.Typical data of surface moving observation stations is used as learning and test samples.Comparison analysis is made between calculated values generated by PSO-SVM method and observed values,and the performance of the proposed method is also compared to that of improved BP neural network.Results indicate that PSO-SVM calculation model has higher precision.A new calculation method for surface subsidence coefficient in mining areas is provided.