基于灰色马尔可夫SCGM(1,1)_C模型的空难人数预测

Apply Gray Markov SCGM(1,1)_C Model to Predict Air Disaster Death Toll

空难事故预测是航空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.结合灰色预测和马尔可夫链理论的优点,提出了一种灰色马尔可夫SCGM（1,1）C模型.用单因子系统云灰色SCGM（1,1）C模型拟合系统的发展变化趋势,并以此为基础进行了马尔可夫预测.对1979～2003年全球空难人数进行了预测分析,结果表明该模型既能揭示了空难人数变化的总体趋势,又能克服了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.

The prediction of air disaster 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. By combining the advantages of both gray prediction and Markov chain theory, a new gray Markov SCGM（1,1）c model is proposed. The single gene system cloud gray SCGM（1,1）c model is applied to imitate the development tendency of the forecast system while Markov prediction is used to forecast the fluctuation along the tendency. Finally, the new model is applied to predict the air disaster death toll of the world from 1979 to 2003. The results show that the new model not only discovers the trend of the air disaster death toll but also overcomes the random fluctuation of data affecting precision.