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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P338.5

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Application of Neural Network BP Model in Forecasting Yearly Average Sediment Concentration in the Wujiang River Basin
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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。

    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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  • 收稿日期:2004-08-02
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