建立了基于实码加速遗传算法（real coded accelerating genetic algorithm, RAGA）的灰色 (grey model,GM(1,1)) 径向基函数 (radial basis function, RBF) 神经网络预测模型。该模型克服了传统 GM(1,1) 模型存在明显系统误差和容易陷入局部最优的缺点 , 具有 GM(1,1) 模型对数据确定性方面把握的优点 , 同时融合了人工神经网络在不确定因素预测方面的优势。 运用该模型对山西工业需水量进行预测 , 预测表明该模型相比单个传统模型具有相对较高的预测精度，验证了 GM(1,1)-RBF 组合模型在中长期需水预测应用中的合理性，对相关政策的制定有一定参考价值。

A GM(grey model)(1,1)-RBF (radial basis function) model based on RAGA(real coded accelerating genetic algorithm) has been established. There exist conspicuously systematical deviations when we are fitting the data using the traditional GM(1,1) model. But the shortcoming has been overcome by the new model. The model has the following advantages: Firstly, it can hold the certainty of the data; what ' s more, the advantages in the uncertainty domain in neural network are interfused. The predicted results indicated that it is more precise than the traditional methods. The scientific rationality of portfolio forecast model used for medium-and long-term forecast respectively is verified . The result will provide a reference in making policy.