人民黄河Issue(2)：30-32,36,4.DOI:10.3969/j.issn.1000-1379.2014.02.010
XPBK神经网络模型的构建与应用
Building and Application of XPBK Neural Network Model
摘要
Abstract
In order to accurately simulate the hydrological processes in a watershed,this paper introduced a new hydrologic model-XAJ Pareto Back Propagation K-nearest neighbour model (XPBK). The XPBK coupled the XAJ model with traditional BP artificial neural network model. Optimiza-tion of the XPBK model parameters was achieved by using the Pareto front set of the K-nearest neighbor algorithm. The XPBK model was applied to hourly streamflow simulations in Chengcun,Dongwan and Fuping watersheds. The results indicate that:a)The performance of XPBK is superior to XAJ in all the watersheds;b)In Chengcun,an average coefficient of determination of both XPBK and XAJ is 0. 97,however,XPBK outperforms XAJ in the Dongwan and Fuping watersheds;c)XPBK model is a promising tool for simulating various hydrologic processes and it achieves higher simulation accuracy in humid regions.关键词
K最近邻算法/Pareto前沿解集法/BP网络/新安江模型/XPBK神经网络模型Key words
K-nearest neighbor algorithm/Pareto solution set/BP neural network/Xinanjiang model/XPBK neural network model分类
天文与地球科学引用本文复制引用
赵丽霞,阚光远,李致家..XPBK神经网络模型的构建与应用[J].人民黄河,2014,(2):30-32,36,4.基金项目
公益性行业（气象）科研专项（GYHY201006037）。 （）
