介绍了RBF神经网络的结构与学习算法,并对黄河河口利津断面的实测资料进行归纳整理,以此建立了用于预测利津断面未来年份水沙通量的RBF神经网络水沙预测模型,对水量和沙量进行了仿真预测。结果表明,该模型对水沙预测结果与实际情况符合较好。 This paper introduces the structure and learning algorithm of RBF neural network and summarizes the measured data of the Lijin section of the Yellow River estuary. Based on this, a RBF neural network model for predicting water and sediment flux in the future Lijin section is established. And sand volume were simulated and predicted. The results show that the model predicts water and sediment results in good agreement with the actual situation.