Unbiased Grey Markov Prediction of Mine Inflow Dynamics
Accurate prediction of mine water inflow is of great significance to mine safety production.To improve the predictive accuracy of mine water inflow in the traditional grey GM(1,1)model as the foundation,build the unbiased grey Markov model,through the analysis of the mine mine water inflow in 2012-2017 raw data,prediction of mine water inflow data from 2018 to 2019,using the unbiased GM(1,1)model instead of the traditional grey GM(1,1)model,through the fitting analysis of mine water inflow data variation characteristics,and on this basis,by using the Markov model to forecast the mine water inflow,updates to the original data,and the four kinds of prediction model to predict result error analysis.The results show that the dynamic unbiased gray Markov model accords with the characteristics of mine inflow data,and the use of gray theory and Markov chain to process dynamic data has obvious advantages,eliminate the inherent errors of the traditional model,and further optimize the model on the basis of the prediction value,which can greatly improve the prediction accuracy.The prediction error of dynamic unbiased gray Markov model is 0.072,0.064 and 0.0245 lower than the average error of traditional,unbiased and gray Markov model,and the prediction accuracy of mine inflow is 98.91％.
safety engineeringmine water inflowdynamic unbiasedgray-Markov
NETL
NSTL
万方数据
