| 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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| 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 |
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| Dynamic Water-Level Neural-Network Forecast Model on Non-Stationary Time Series |
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XUE Lianqing1, CUI Guangbai1, CHEN Kaiqi2
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1.Hohai University, Nanjing 210098, P.R.China;2.Institute of Water Conservancy and Hydropower Research of China, Beijing 100044, P.R.China
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| 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 |
