| BP神经网络洪水预报模型在洪水预报系统中的应用 |
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| 引用本文： | 胡健伟,周玉良,金菊良.BP神经网络洪水预报模型在洪水预报系统中的应用[J].水文,2015,35(1):20-25. |
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| 作者姓名： | 胡健伟 周玉良 金菊良 |
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| 作者单位： | 水利部水文局;合肥工业大学土木与水利工程学院 |
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| 基金项目： | 国家自然科学基金(51109052);水利部公益性行业科研专项经费(201001045) |
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| 摘 要： | 采用相关分析法,在区域降水、观测断面流量(或水位)因子中识别出影响预报断面径流过程的主要变量,在多个观测断面的数据均为流量情况下,采用基于时延组合的合成流量为影响预报断面径流过程的变量,采用自相关分析法,识别出影响预报断面径流过程的前期流量(或水位),以这些变量为BP神经网络模型的输入,以预报断面的流量(或水位)为模型的输出,在BP神经网络隐层节点数自动优选的基础上,构建了基于BP神经网络的洪水预报模型。将模型载入中国洪水预报系统中,应用结果表明:模型在历史洪水训练样本具有一定代表性的情况下,可获得较高的预报精度。
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| 关 键 词： | 洪水预报 人工神经网络 BP算法 洪水预报系统 应用 |
| 收稿时间： | 2014/6/5 0:00:00 |
Flood Forecasting Model on BP Neural Networks and Its Application in Flood Forecasting Systems |
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| HU Jianwei,ZHOU Yuliang,JIN Juliang.Flood Forecasting Model on BP Neural Networks and Its Application in Flood Forecasting Systems[J].Hydrology,2015,35(1):20-25. |
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| Authors: | HU Jianwei ZHOU Yuliang JIN Juliang |
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| Institution: | HU Jianwei;ZHOU Yuliang;JIN Juliang;Bureau of Hydrology,MWR;School of Civil Engineering, Hefei University of Technology; |
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| Abstract: | Correlation analysis technique was used to identify main influence factors of runoff processes for prediction river section from regional precipitation, discharge or stage of river survey section, especially when the data type of river survey sections was discharge, the combined discharge obtained on combination of lag times of runoff between survey sections and prediction section was selected as influence fac tors, and the auto-correlation analysis technique was adopted to identify influence factor from preceding discharge or stage process of prediction section. And then the influence factors were used as input of networks, and the discharge or stage of prediction section was used as output of networks, with node number of hidden layer acquire by trial and error automatically, the flood forecasting model based on BP neural networks was established. Then the established model was loaded in national flood forecasting systems. The application results show that satisfactory forecasting effects are acquired when there are some representative flood processes in the training samples. |
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| Keywords: | flood forecasting artificial neural networks back propagation algorithm flood forecasting system application |
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