| 基于互信息量与BP神经网络的中长期径流预报方法研究 |
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| 引用本文： | 卢迪,周惠成.基于互信息量与BP神经网络的中长期径流预报方法研究[J].水文,2014,34(4):8-14. |
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| 作者姓名： | 卢迪 周惠成 |
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| 作者单位： | 大连理工大学建设工程学部水利工程学院 |
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| 摘 要： | 针对中长期径流预报因子的选择问题,采用互信息量方法筛选预报模型输入因子,在BP神经网络模型中,分别用均方误差和互信息量作为目标函数,衡量因子复合相关关系,优化选择最终预报因子并应用于碧流河汛期径流预报中。结果表明,基于互信息量筛选的预报因子与BP神经网络模型相结合,可有效识别多个预报因子与预报量间的复合相关性,对中长期径流预报因子的选择有很好参考价值。
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| 关 键 词： | 互信息 神经网络 中长期径流预报 |
| 收稿时间： | 2013/8/15 0:00:00 |
Medium and Long-term Runoff Forecasting Based on Mutual Information and BP Neural Network |
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| LU Di,ZHOU Huicheng.Medium and Long-term Runoff Forecasting Based on Mutual Information and BP Neural Network[J].Hydrology,2014,34(4):8-14. |
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| Authors: | LU Di ZHOU Huicheng |
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| Institution: | School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China |
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| Abstract: | As for the medium and long-term runoff forecasting factors selection, this paper introduced mutual information (MI) to select the subset of factors from numerous meteorological factors into back-propagation neural network (BPN) model. In the model, mean square error (MSE) and MI were presented as objective functions respectively to measure factors compound correlation for the purpose of selecting optimal forecasting factors. The study was applied to forecast flood season runoff of the Biliuhe reservoir. The results show that using MI to select the subset and combining MI with BPN model can identify the correlation between runoff and its affecting factors effectively. The methods of factors selection may provide a good reference for medium and long-term runoff forecasting. |
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| Keywords: | mutual information neural network medium and long-term runoff forecasting |
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