| 基于BP神经网络的河川年径流量预测 |
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| 引用本文： | 顾海燕,徐文科,于雷.基于BP神经网络的河川年径流量预测[J].东北林业大学学报,2007,35(10):83-85. |
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| 作者姓名： | 顾海燕 徐文科 于雷 |
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| 作者单位： | 东北林业大学,哈尔滨,150040 |
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| 摘 要： | 运用人工神经网络模型对松花江流域年径流量径流序列做出预报,表明了人工神经网络模型在水文预报中具有一定的优势。通过对基本BP网络算法和L-M算法的比较工作,得到了适合该神经网络模型的训练算法,既L-M算法,提高了预报的精度。以松花江流域哈尔滨站年径流量实测序列为研究对象,在数值试验的基础上找到了适合于松花江流域哈尔滨站年径流序列预报的人工神经网络预报模型结构,提高了该模型的预报准确性。
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| 关 键 词： | 人工神经网络 BP神经网络 L-M算法 年径流量预测 |
| 修稿时间： | 2006-12-20 |
Prediction of Annual Runoff in Songhua River Valley Based on BP Neural Networks |
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| Gu Haiyan,Xu Wenke,Yu Lei.Prediction of Annual Runoff in Songhua River Valley Based on BP Neural Networks[J].Journal of Northeast Forestry University,2007,35(10):83-85. |
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| Authors: | Gu Haiyan Xu Wenke Yu Lei |
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| Institution: | College of Science, Northeast Forestry University, Harbin 150040, P. R. China |
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| Abstract: | Artificial neural network(ANN)model was used to predict the annual runoff of Songhua River Valley.It demonstrates that the ANN model is predominant in hydrological prediction.L-M algorithm with higher prediction accuracy is proved to be the optimal training algorithm for the ANN model compared with BP algorithm.Taking the actual series of annual runoff in Harbin station of Songhua River Valley as the research object,the framework of the optimal ANN prediction model is established,which can enhance the prediction accuracy. |
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| Keywords: | Artificial neural networks BP neural networks L-M algorithm Annual runoff prediction |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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