Seepage real-time forecast model based on genetic neural network

YAN Bin; ZHOU Jing

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Seepage real-time forecast model based on genetic neural network

更新时间：2026-06-25

- Seepage real-time forecast model based on genetic neural network
- Vol. 27, Issue S1, (2006)
- 作者机构：
  
  1. 大连理工大学土木水利学院,辽宁,大连,116024
  2. 沈阳农业大学水利学院,辽宁,沈阳,110161
- 作者简介：
- 基金信息：
- DOI：
  CLC： O224
- Online First：15 December 2006，
  
  Published：2006
- 稿件说明：
移动端阅览
YAN Bin, ZHOU Jing. Seepage real-time forecast model based on genetic neural network[J]. 2006, 27(S1).
DOI：

YAN Bin, ZHOU Jing. Seepage real-time forecast model based on genetic neural network[J]. 2006, 27(S1). DOI：

摘要

考虑渗流的主要影响因子，运用遗传神经网络建立了大坝渗流实时预报模型。该模型具有再学习能力，在应用过程中，可以用新的观测资料对模型不断地进行学习训练，且随着样本的积累，模型预报精度不断提高，预报速度很快，因而完全满足实时预报的需要。利用该模型对丰满大坝横向扬压力进行了建模预报，预报值与实测值基本吻合，预测精度高，证明该方法用于大坝渗流实时预报的有效性和实用性。

Abstract

In terms of main influencing factors of seepage

a seepage real-time forecast model is established based on genetic algorithm and neural network. The model can be trained sequentially with new observed data in further application; and the accuracy of the model can be improved in less training time with the accumulation of samples. Thus

it can satisfy the demand of real-time forecast. The predicted results agree well with the measured ones in the forecast of transverse uplift pressure of Fengman Dam so as to indicate that the proposed method has high prediction accuracy and is valid and practical for seepage real-time forecast.

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