STUDY ON GREY MARKOV PREDICTION MODEL FOR OLD GOAF RESIDUAL SUBSIDENCE
Wang Zhengshuai 1,2) ;and Deng Kazhong 1,2)
1Jiangsu Key Laboratory of Resources and Environmental Information Engineering,CUMT, Xuzhou 2211162School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116
Abstract：On the basis of analyzing the shortcomings of GM(1,1) in predicting fluctuant residual subsidence series of old goaf, a new model for residual subsidence prediction named grey Markov prediction model (GMMarkov) was proposed. In this model, particle swarm optimization (PSO) is used to optimize the parameters of background and initial disturbance values of GM(1,1) so that the trend could be predicted and separated fully from the subsidence series; then, Markov chains is selected to correct the prediction value. The model of GMMarkov was applied to predict the residual subsidence of an old goaf and the prediction values were compared with that of GM(1,1). Compared with GM(1,1), the result of GMMarkov model show good qualities in terms of prediction accuracy and stability.
