长江科学院院报 ›› 2011, Vol. 28 ›› Issue (1): 21-24.

• 水资源与环境 • 上一篇    下一篇

一种基于 BP 神经网络的江河潮位短期预测

何 峰 1 ,王瑞荣 1 ,王建中 1 ,薛安克 1 ,谢发权 2 ,何晓洪 2 ,孙映宏 2   

  1. China1. 杭州电子科技大学,杭州 310018 ; 2. 杭州水文水资源监测总站,杭州 310014
  • 出版日期:2011-01-01 发布日期:2012-09-06

A Kind of Short-term Height-Prediction of Tidal Level of Rivers Based on BP Neural Network Model

HE Feng 1, WANG Rui-rong 1, WANG Jian-zhong 1, XUE An-ke 1, XIE Fa-quan 2,  HE Xiao-hong 2, SUN Ying-hong 2   

  1. 1.Hangzhou Dianzi University, Hangzhou 310018, China ;  2.Hangzhou Hydrology and Water Resources Monitoring Station, Hangzhou 310014,
  • Online:2011-01-01 Published:2012-09-06

摘要: 江河涌潮与海洋潮汐相比较,其潮位和潮时有着与海洋潮汐规律相同的一面,但它与海洋潮汐所不同的是,其更容易受到天文、气象等因素的影响,有着异于海洋潮汐的不稳定性。涌潮的潮位值变化就是各种因素综合结果的表现。通过借助 BP 神经网络可以逼近任意非线性函数的能力和特点,构建一个用于短期预测潮水潮位的模型,利用一小部分潮位的历史数据作为训练样本对构建出的 BP 神经网络模型进行样本训练,用其它的潮位历史数据对模型进行了预测验证。模型验证结果表明,这个 BP 神经网络预测模型能够对潮水潮位进行有效的短期预测,其性能指标符合要求。

关键词: 涌潮 ,  ,  , BP 神经网络 , 潮位 , 短期预测

Abstract: In comparison between the tide bore of rivers and the tide of ocean, the occurrence  time and height of the river tide bore has the same feature as the ocean tide ; but the river tide bore is influenced more easily by astronomy, weather and other factors, so it has the instability that is different from the ocean tide ; the tide level of the tidal bore is the comprehensive result influenced by all kinds of factors. Since the BP neural network can approach any non-linear function, a short-term prediction model of the tidal height has been constructed. Some historical tidal data, as a training sample, are used to train the model, and others are used to predict the tidal data. Finally, the validity and the feature of the prediction model have been verified.

Key words: tide bore ,  , BP neural network  , tide level ,  , short-term prediction

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