摘要
喀斯特流域降雨-径流响应是一个非线性过程,分析确定地下河流量过程的主要影响因子对喀斯特流域水文过程模拟具有重要意义。本文利用普定后寨河流域实测降雨、径流系列资料,采用神经网络权重分析法确定该流域的人工神经网络模型结构为两个隐含层、三个输入变量,该人工神经网络模型结构可以保持降雨-径流模拟的稳定性。模型经交叉训练与验证,训练期效率系数(N SC)达0.9以上,验证期N SC达0.88以上。说明神经网络权重分析法能够较好地确立预报因子与预报对象的关系,为喀斯特流域降雨-径流模拟提供一种有效的分析手段。
Rainfall-runoff response in karst basin is a non-linear process.Determination of the major factors influencing underground river flow by proper nonlinear analysis method is very important for simulating karst hydrological processes.In this study,the observed rainfall and flow discharge data in the Houzhai catchement of Puding County was used.The BP model structure of two hidden layers and three inputs in the study catchment was determined by the neural network weight analysis.This structure is able to keep stability of the rainfall-runoff simulation.Cross training and validation results of the BP model show that efficient coefficient（NSC） is over 0.9 during the training period,and NSC is over 0.88 during the validation period.Therefore,the neural network weight analysis can be used to determine the relationship between forecast object and its influencing factors.The artificial neural network model offers an efficient way for rainfall-runoff simulation in karst basin.
出处
《中国岩溶》
CAS
CSCD
2009年第4期375-379,共5页
Carsologica Sinica
基金
国家重点基础研究(973)资助项目(2006CB403200)
国家自然科技基金重点项目(40930635)
关键词
喀斯特
神经网络
权重分析
降雨-径流模拟
模型结构
普定后寨河流域
贵州
karst
neural network
weight analysis
rainfall-runoff simulation
model structure
Houzhai catchement in Puding
Guizhou
