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引用本文:薛联青,崔广柏,陈凯麒.非平稳时间序列的动态水位神经网络预报模型.湖泊科学,2002,14(1):19-24. DOI:10.18307/2002.0103
XUE Lianqing,CUI Guangbai,CHEN Kaiqi.Dynamic Water-Level Neural-Network Forecast Model on Non-Stationary Time Series. J. Lake Sci.2002,14(1):19-24. DOI:10.18307/2002.0103
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非平稳时间序列的动态水位神经网络预报模型
薛联青1, 崔广柏1, 陈凯麒2
1.河海大学水环境学院, 南京 210098;2.中国水利水电科学研究院, 北京 100044
摘要:
水文预报系统是一个复杂的非线性动力学过程,站点水位受各种因素的影响不仅呈现出非平稳动态随机变化特性,而且各因素间的关系也很难确定.淮河流域五河站水位由于受到洪泽湖回水影响及季节性的影响,也呈现出这一动力学的非平稳特性,因此本文在考虑了相关站点和回水影响的基础上,建立了一种多站变量时间序列的神经网络预报模型,预报结果表明该方法预测效果较好,运行简单.
关键词:  时间序列  预报模型  水位  回水影响  神经网络
DOI:10.18307/2002.0103
分类号:
基金项目:
Dynamic Water-Level Neural-Network Forecast Model on Non-Stationary Time Series
XUE Lianqing1, CUI Guangbai1, CHEN Kaiqi2
1.Hohai University, Nanjing 210098, P.R.China;2.Institute of Water Conservancy and Hydropower Research of China, Beijing 100044, P.R.China
Abstract:
Hydrology prediction is a complexnon-linear dynamic process and the station water-level often shows dynamic changing character owing to all kinds of factors.In the Huaihe Basin Wuhe station water-level will be influenced by the backwater influence of Hongze lake and shows the non-statinoary changing.In the paper based on the neural-network model of time series and the data characteristics of hydrology a non-stationary multi-station variable dynamic sequence prediction model is made by using artificial neural-network and practised in Wuhe station water-level prediction of Huaihe River.The calculation results indicates that the model is not only reasonable but also its predicting period is longer.It is valuable when being used in practices.
Key words:  time series  prediction model  water-level  backwater influence  ANN
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