运用神经网络理论研究钱塘江涌潮的影响因素

 在定性分析涌潮主要影响因素的基础上，借助BP神经网络建立了盐官站潮头高度的预测模型，经检验该模型具有较好的泛化能力。结合正交试验设计理论，定量地分析了各因素对涌潮的影响程度。研究结果表明：潮头高度与下游潮汐呈正相关关系，而与河道地形呈负相关关系，当径流流量小于7 000 m3/s时，潮头高度与上游径流也呈正相关关系，超过此范围，则呈负相关关系。

Analysis of Influence Factors for Qiantang Bore Based on Neural Network Theory

Based on qualitatively analyzing influence factors, the model for predicting the height of tidal bore at Yanguan is established by using BP neural network. The comparison of the simulated results with the observed data shows that the model has good generalization ability. In combination with the theory of orthogonal experiment design, the major affecting factors such as tide, runoff and river topography are quantitatively analyzed. It is found that the height of tidal bore is positively related to the tide, and negatively related to the river topography. The height of tidal bore is positively related to the runoff when the discharge is less than 7 000 m^3/s. However they are negative correlation relationship if the discharge is greater than 7 000 m^3/s.