Abstract
The prediction of mine accident is the basis of aviation safety assessment and decision making. Gray prediction is suitable for such kinds of system objects with few data, short time, and little fluctuation, and Markov chain theory is just suitable for forecasting stochastic fluctuating dynamic process. Analyzing the coal mine accident human error cause, combining the advantages of both Gray prediction and Markov theory, an amended Gray Markov SCGM(1,1)c model is proposed. The gray SCGM(1,1)c model is applied to imitate the development tendency of the mine safety accident, and adopt the amended model to improve prediction accuracy, while Markov prediction is used to predict the fluctuation along the tendency. Finally, the new model is applied to forecast the mine safety accident deaths from 1990 to 2010 in China, and, 2011–2014 coal accidents deaths were predicted. The results show that the new model not only discovers the trend of the mine human error accident death toll but also overcomes the random fluctuation of data affecting precision. It possesses stronger engineering application.
Highlights
Coal is an important basic energy and raw material in China, and it accounts for 70% of primary energy [1]
The gray SCGM(1,1)c model is applied to imitate the development tendency of the mine safety accident, and the amended model is to improve prediction accuracy while Markov prediction is used to predict the fluctuation along the tendency, so as to further improve the prediction accuracy on random volatile accident data
Markov theory to correct the SCGM(1,1)c prediction model of coal mine accident deaths can better solve the variability and randomness of accidents caused by human errors to improve the prediction accuracy
Summary
Coal is an important basic energy and raw material in China, and it accounts for 70% of primary energy [1]. According to HFACS analysis methods in the coal mine accident, human factors are relatively complex and changeable [6], so more affecting data are unknown; it is a gray system. Low prediction accuracy can be avoided due to historical data lack or inaccuracy by applying the Gray SCGM(1,1)c model to predict the coal mine safety accidents. Using the regression models and empirical models to predict accident requires large amounts of historical data. Due to human error is affect by many factors, and in a dynamic time-varying system with low accident data and the non-line random changes, so it is not suitable to use these methods for prediction. The gray SCGM(1,1)c model is applied to imitate the development tendency of the mine safety accident, and the amended model is to improve prediction accuracy while Markov prediction is used to predict the fluctuation along the tendency, so as to further improve the prediction accuracy on random volatile accident data
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