| 应用人工神经网络BP模型预测乌江流域年平均含沙量 |
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| 引用本文： | 陈集中.应用人工神经网络BP模型预测乌江流域年平均含沙量[J].水文,2005,25(4):6-9. |
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| 作者姓名： | 陈集中 |
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| 作者单位： | 湖南省长沙水文水资源勘测局,湖南,长沙,410014 |
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| 摘 要： | 引入人工神经网络BP模型对流域产沙进行了定量预测。根据石坝子水文站断面以上乌江流域的土壤、地质、地貌在一定时间范围内具有相当稳定的特性，选取植被覆盖率、年降雨量、年平均流量和年汛期径流量共4个代表植被、气候和水流特性的主要因子对流域年平均含沙量进行了建模预测。优化得出的BP网络模型不仅拟合精度高，而且预测效果好，这为泥沙方面的定量研究提供了一条新的途径，也为石坝子水文站停测泥沙测验项目提供了科学依据。
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| 关 键 词： | 人工神经网络BP模型 年平均含沙量 预测 乌江流域 |
| 文章编号： | 1000-0852（2005）04-0006-04 |
| 收稿时间： | 2004-08-02 |
| 修稿时间： | 2004年8月2日 |
Application of Neural Network BP Model in Forecasting Yearly Average Sediment Concentration in the Wujiang River Basin |
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| Chen JiZhong.Application of Neural Network BP Model in Forecasting Yearly Average Sediment Concentration in the Wujiang River Basin[J].Hydrology,2005,25(4):6-9. |
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| Authors: | Chen JiZhong |
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| Abstract: | This paper gives an approach on quantitatively forecasting catchment sediment yield with neural network BP model. Because soil, geography and physiognomy have a considerable stability above the cross-section at the Shibazi Station in the Wujiang River Basin in a period, catchment sediment concentration has been forecasted by using four essential factors of plant-recover-rate, yearly rainfall, yearly average flow and yearly run-off quantity. Optimized BP model not only has high simulation accuracy, but also gives a good forecasting effect. This has provided a new method for quantitative research and scientific proof for the project of stopping sediment measurement at the Shibazi Hydrometric Station. |
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| Keywords: | neural network BP model yearly average sediment concentration forecasting the Wujiang River Basin |
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