灰色马尔科夫模型在年降水量预测中的应用

Application of Annual Precipitation Prediction of Grey Markov Model

在遵循新信息优先的灰色理论前提下,分别将第一个时刻的数据x(1)(1)、最后时刻的数据x(1)(n)以及前一个时刻的数据x(1)(k-1)作为初始条件建立GM(1,1)模型,并采取加权的方式对各模型的预测结果进行求和,得到改进后的GM(1,1)模型预测结果。然后,利用马尔科夫模型对优化灰色模型预测结果进行修正。实例应用结果表明:灰色马尔科夫模型预测精度比传统灰色模型有明显提高,与实际降水量数据的拟合程度更好。

In the premise of following new information priority of grey theory,respectively selecting the first time data x（1）（1）、the last time data x（1）（n）and the previous time data x（1）（k - 1）as initial condition of grey model,it calculated the prediction results of the model by weighting the three different results and obtained the improved GM（1,1）model results. Then it modified the optimized grey model by using the Markov model. The results show that the grey Markov model has higher accuracy than that of the traditional grey model and fitted the actual rainfall data better.