基于改进灰色GM(1,1)马尔柯夫模型的煤矿事故百万吨死亡率预测

煤矿安全事故预防和控制是煤矿安全评价和决策的基础.灰色预测适合于时间短、数据量少和波动不大的系统对象,而马尔可夫链理论适用于预测随机波动大的动态过程.结合灰色预测GM(1,1)模型和马尔可夫链理论的优点,提出了一种改进的灰色马尔可夫GM(1,1)模型.利用改进的GM(1,1)模型进一步拟合煤矿人因失误事故的发展变化趋势,并以此为基础进行马尔柯夫预测,提高预测效果.以2000-2010年全国煤矿事故百万吨死亡率为例进行了预测分析,结果表明模型既能揭示煤矿人因失误事故百万吨死亡率变化的总体趋势,又能克服随机波动性数据对预测精度的影响,具有较强的工程实用性,并对煤矿人因失误安全事故的预测和控制有一定的实际意义.

Death Rate per Million Ton Prediction of Coal Mine Accidents Based on Improved Gray Markov GM(1,1) Model

The prediction of mint 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.Combining the advantages of both Gray prediction GM(1,1) model and Markov chain theory,a weigh Gray Markov GM(1,1) model was proposed.The improved gray GM(1,1) model was applied to imitate the development tendency of the mine human factors accident while Markov prediction is used to predict the fluctuation along the tendency to improve forecast result.Finally,the new model is applied to forecast the death rate per million ton of coal mine accidents in China from 1990 to 2010.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 and practical significance to predict and control human error accident in the coal mine.