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调水调沙对黄河下游通航的影响*
引用本文:严 军,刘 蛟,葛高岭,杨先波.调水调沙对黄河下游通航的影响*[J].水运工程,2012(4):143-148.
作者姓名:严 军  刘 蛟  葛高岭  杨先波
作者单位:1. 华北水利水电学院水利学院,河南郑州,450011
2. 中交天津港航设计研究公司,天津,300461
3. 长江武汉航道工程局,湖北武汉,430010
基金项目:国家自然科学基金重点项目(51039004);河南省自然科学基金(2011A570006)
摘    要:介绍黄河下游航道的现状,根据实测资料分析调水调沙前后河道主槽宽深变化和流量水位变化的规律。结果表明:调水调沙后,主槽的冲刷和展宽明显,过水能力增加,通航条件得到改善。利用2001—2010年小浪底、花园口、高村、艾山和利津站水文资料,建立了水位、流量和过流面积1对2的神经网络模型,各站点模型表现的相关性良好,预测结果验证了调水调沙使得下游河道在宽度和深度上都有所增加,有利于通航。

关 键 词:调水调沙  黄河下游  通航条件  神经网络

Effects of water and sediment regulation on navigation in the lower Yellow River
YAN Jun,LIU Jiao,GE Gao-lin,YANG Xian-bo.Effects of water and sediment regulation on navigation in the lower Yellow River[J].Port & Waterway Engineering,2012(4):143-148.
Authors:YAN Jun  LIU Jiao  GE Gao-lin  YANG Xian-bo
Institution:1. Water Conservancy College of North China Water Conservancy and Electric Power Univ., Zhengzhou 450011, China;2. Traffic China Tianjin Harbor and Sea-channel Design Co., Tianjin 300461, China;3. Changjiang Wuhan Waterway Engineering Bureau, Wuhan 430010, China
Abstract:The status of navigation in the lower Yellow River is introduced. Based on the field data, we analyze the law of the changes which of width, depth, water level and discharge before and after the water and sediment regulation in the lower Yellow River. The results indicate that after the water and sediment regulation, the erosion and broadening of the main channels are obvious, the ability of over-water is enlarged, and the condition of navigation is improved. With the field data from 2001 to 2010 in hydrological stations of Xiaolangdi, Huayuankou , Gaocun, Aishan and Lijin, we build the neural network model which includes water level, discharge and flow area, and the pertinence of the model in every hydrological station is good. The prediction result validates that the water and sediment regulation makes both the width and depth of the main channels in the downstream increase, which is advantageous to navigation.
Keywords:water and sediment regulation  lower Yellow River  navigation condition  neural network
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