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.
出处
《水科学进展》
EI
CAS
CSCD
北大核心
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
关键词
非线性映射
流域产流产沙
BP网络预报模型
non-linear maping
watershed runoff and sediment yielding
BP networks forecasting model
