用灰色马尔可夫模型预测水上交通事故量

Forecasting of Maritime Accident Quantity with Grey Markov Model

灰色GM(1,1)是一种水上交通事故量预测模型。这种模型不适合长期的、随机和波动性较大的数据序列预测。马尔可夫模型适合描述随机波动性较大的预测问题。本文将两模型结合,形成一个灰色马尔可夫预测模型。按特定的状态划分方法,先用灰色GM(1,1)预测模型进行预测,再用马尔可夫模型预测结果进行优化,使预测精度大大提高。文中给出两个例子,算例证明了该模型的诸多优点。

Grey GM(1,1) is a model for forecasting maritime accident quantity. This model is not suited to forecast the accidents in long term with randomness and great changed data. But, Markov model is advantaged to treat with problems with these characteristics. Two models are combined together to form a model——grey Markov model in this paper. According to the special state divided ways proposed in this paper, grey GM(1,1) model is used to optimize the data first, then, Markov model is used to optimize the results from the grey GM(1,1) model again. The grey Markov model makes the optimum result much more precise. Two examples given have proved the advantages.