摘要
本文采用人工神经网络方法分析了黄河下游汛期和非汛期水沙条件对平滩流量的影响,建立了黄河下游4个测站的人工神经网络模型,并重点分析了非汛期水沙因子对平滩流量的作用。结果表明,人工神经网络模型能够同时考虑汛期和非汛期水沙条件对平滩流量的综合作用。当平滩流量计算中加入非汛期影响因子后,各站计算精度均有较大幅度提高。因此,对于黄河下游平滩流量的计算,不仅要考虑汛期水沙条件的影响,非汛期水沙条件的影响也是不可忽视的。
Estimation of bankfull discharge is crucial to determination of water regulating volume and sediment load in the lower Yellow. This paper studies the effects of flow and sediment load in non-flood season on the bankfull discharge at four hydrological stations on this river. BP neural network was used to develop bankfull discharge models that take the control factors of flow and sediment discharge as input conditions. To estimate these effects, two inputs were compared： given volumes of water and sediment load transported only in flood season, the same volumes transported in one hydrological year. The results show that the effects of non-flood season transports on the bankfull discharge are not negligible. Consideration of the non-flood season roles of water flow and suspended load can significantly improve the modeling accuracy of the flows at all the four stations.
出处
《水力发电学报》
EI
CSCD
北大核心
2013年第1期132-138,共7页
Journal of Hydroelectric Engineering
基金
国家自然科学基金项目(51109116)
国家水专项(2009ZX07212-005-3
水利部公益项目(200901016-03)
中荷战略科学联盟计划项目(2008DFB90240)
关键词
河流泥沙工程学
河床演变
平滩流量
神经网络
黄河下游
非汛期
river sedimemtation engineering
fluvial process
bankfull discharge
artificial neural networks
lower Yellow River
non-flood season
