流域次降雨侵蚀产沙的BP神经网络模拟  

Back Propagition Neural Network Simulation on Sediment Yield of Watershed Under Single Rainfall

作　　者：侯建才[1] 李占斌[1] 李勉[2] 王民[1] 

机构地区：[1]西安理工大学,陕西西安710048 [2]黄河水利委员会黄河水利科学研究院,河南郑州450003

出　　处：《水土保持通报》2007年第3期79-83,共5页Bulletin of Soil and Water Conservation

基　　金：国家自然基金黄河联合基金(50479066)

摘　　要：在分析黄土高原韭园沟流域多年观测资料的基础上,应用BP神经网络建模方法,建立了流域次降雨侵蚀产沙的神经网络模型。通过输入模型变量流域次降雨量、平均降雨强度、径流深和洪峰流量模数,对流域次降雨侵蚀产沙量进行了训练和预测。预测结果表明,所建BP神经网络模型预测精度较高,可近似揭示复杂非线性流域次降雨侵蚀产沙系统的产沙规律,为建立较高预报精度的黄土高原流域次降雨侵蚀产沙预报模型提供了依据。On the basis of the analyses of twenty year data observed in Jiuyuangou watershed on the Loess Plateau, a model of sediment yield of watershed under single rainfall is propounded through the application of back propagition artificial neural network. The network model is trained and predicted by input rainfall, average rainfall intensity, runoff depth and flood-peak modulus. The predicted results show that the network model has good precision and accurately reflects the laws of nonlinear sediment yield of watershed under single rainfall. It provides a new foundation to establish the predicted model of sediment yield of watershed under single rainfall on the Losses Plateau.

关 键 词：次降雨 BP神经网络 侵蚀产沙 模拟 

分 类 号：S157.1[农业科学—土壤学]