流域产流产沙BP网络预报模型的初步研究  

Preliminary Research on the BP Networks Foreasting Model of Watershed Runoff and Sediment Yielding

作　　者：张小峰[1] 许全喜 裴莹[3] 

机构地区：[1]武汉大学水利水电学院,湖北武汉430072 [2]长江水利委员会水文局,湖北武汉430010 [3]水利部水文局,北京100053

出　　处：《水科学进展》2001年第1期17-22,共6页Advances in Water Science

基　　金：教育部留学回国人员科研启动基金资助项目

摘　　要：运用 BP神经网络模型的基本原理 ,以流域降水条件为基本因子 ,建立了流域产流产沙 BP网络预报模型。该模型能用于定量分析流域人类活动因素对流域产流产沙的影响。西汉水、大通江、香溪河流域资料验证表明 ,模型基本合理、可靠。In this paper, considering the radical principle of neural networks and acting rainfall condition as the main affecting factors, a back propagation (BP) networks model of watershed runoff and sediment yielding is discussed.The model has satisfactory learning and generalization performance, and it may be used to value the human-action influence on runoff and sediment yielding in a watershed.The results identified by Xihanshui, Datongjiang and Xiangcihe basins’s observed data indicate that the model are satisfactory.

关 键 词：非线性映射 流域产流产沙 BP网络预报模型 

分 类 号：TV142[水利工程—水力学及河流动力学]