干旱区生态环境知识资源中心(Arid): 基于径流分类的流域降雨径流过程动态神经网络建模

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	基于径流分类的流域降雨径流过程动态神经网络建模
其他题名	Rainfall-runoff Simulation Based on Runoff Classification Using Dynamic Artificial Neural Networks
	邵月红 1; 林柄章 1; 刘永和 2
来源期刊	地理科学
ISSN	1000-0690
出版年	2012
卷号	32 期号:1 页码:74-80
中文摘要	利用聚类分析,将径流序列分为不同类型的子径流序列,对这些子序列建立神经网络模型,采用Elman动态神经网络对沂沭河流域上游临沂子流域日径流量进行预测分析,通过与不加分类的总体神经网络的模拟结果进行对比分析。确定性系数、相关系数、平均相对误差和平均相对均方根误差4个统计指数及流域径流过程线和次洪误差分析结果都表明:Elman动态神经网络能够对日径流量进行较好模拟,但基于径流分类的降雨径流模型表现出更优良性能,能较大程度提高径流模拟精度。
英文摘要	A runoff sequence is classified into several sub-runoff sequences with cluster analysis and practical application,and local dynamic neural network for each sub-runoff sequence is performed separately.An Elman recurrent neural network model(ENN) is constructed and applied for the daily runoff forecast in the Linyi sub-catchment of the upper Yishu river basin in this paper.In order to further evaluate the performance of local Elman neural network(LENN),global Elman neural network(GENN) is applied as a comparison at the same time in the study region.Based on analysis indexes such as Nash-Sutcliffe coefficient,correlation coefficient,mean relative error and root mean relative square error,the results of daily runoff and flooding processes are attained.It suggested that Elman recurrent neural network model has a high accuracy of forecast on the rainfall-runoff dynamic process and the maximum peak flow and peak occurrence time,whether it is configuration samples or evaluation samples.However,local Elman neural network based on runoff classification is more suitable and efficient to daily runoff forecasting than global Elman neural network,especially during the period of arid season and semi-humid season.The simulation precision of different periods decreases in the order of arid season,semi-humid season and humid season.Elman recurrent neural network model is feasible to effectively simulate the hydrological dynamic characteristic of the daily runoff and to reflect the complex nonlinear runoff regular pattern in the basin as a promising and efficient method.In order to further evaluate superiority of this method,longer series of data,more regions and hydrology model are need to study and analysis because of the highly nonlinear,spatial and temporal heterogeneity and dynamic uncertainty of rainfall-runoff process.
中文关键词	径流分类 ; 聚类分析 ; Elman神经网络 ; 降雨径流 ; 局部神经网络 ; 总体神经网络
英文关键词	runoff classification cluster analysis,Elman neural network rainfall-runoff local neural network global neural network
语种	中文
国家	中国
收录类别	CSCD
WOS类目	AGRICULTURAL ENGINEERING
WOS研究方向	Agriculture
CSCD记录号	CSCD:4440507
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/227828
作者单位	1.南京信息工程大学应用水文气象研究院, 南京, 江苏 210044, 中国; 2.河南理工大学资源环境学院, 焦作, 河南 454000, 中国
推荐引用方式 GB/T 7714	邵月红,林柄章,刘永和. 基于径流分类的流域降雨径流过程动态神经网络建模[J],2012,32(1):74-80.
APA	邵月红,林柄章,&刘永和.(2012).基于径流分类的流域降雨径流过程动态神经网络建模.地理科学,32(1),74-80.
MLA	邵月红,et al."基于径流分类的流域降雨径流过程动态神经网络建模".地理科学 32.1(2012):74-80.