Abstract: Time series of hydrological forecasting data mining,based on grey theory and combined grey theory and neural network model.Original sequences transformed by logarithm-cube to meet the overlay need at first,then predicted by the model of grey forecasting model GM（1,1）.Due to the GM（1,1） belongs to linearity forecasting,introduces the neural networks combining with grey forecasting model to advance forecasting precision compare to the grey model.Experiments based on DuJiangyan Min-jiang River prove that the forecasting effect and rousting of the hybrid forecasting model is superior to traditional forecasting models.

摘要： 基于灰色理论和灰色神经网络组合预测模型,对水文时间序列进行数据挖掘。对原始序列首先进行了对数-方根变换,使得数据序列满足灰色理论的覆盖条件,采用灰色预测模型GM（1,1）,对数据序列进行预测,由于灰色预测属于线性预测,因此将灰色预测模型与神经网络模型相结合,提高了预测精度。以都江堰岷江来水数据为原始数据进行实际预测,实验证明,这种组合模型的预测效果优于传统预测模型。