| 基于机器学习的水位流量关系模型参数估计 |
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| 引用本文： | 江竹,宋文武.基于机器学习的水位流量关系模型参数估计[J].水文,2013,33(1):74-78. |
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| 作者姓名： | 江竹 宋文武 |
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| 作者单位： | 西华大学能源与环境学院,四川成都,610039 |
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| 基金项目： | 四川省流体机械重点实验室资助(SBZDPY-11-5);西华大学高校重点项目(Z1120413) |
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| 摘 要： | 为了克服经典水位流量关系模型在刻画河流动态变化特性时所存在的局限性,提出采用局部加权回归算法估计模型参数;为了提高参数估计精度以及流量的计算效率,提出一种聚类树加权回归方法.首先对训练样本进行聚类,然后使用k-最近邻方法将新的水位样本划分进最恰当的聚类中,最后估计河流日流量.该方法在估计过程中,避免了不相关信息的干扰,从而提高了日流量数据估计的效率和精度.利用某水文站的实测数据对方法进行测试,仿真结果表明,方法估计精度高,为水位流量关系模型参数估计提供了新的有效方法.
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| 关 键 词： | 水位流量关系 参数估计 局部加权回归 聚类树 k-最近邻 |
| 收稿时间： | 2011/12/5 0:00:00 |
Parameter Estimation of Stage-discharge Relationship Based on Machine Learning |
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| JIANG Zhu,SONG Wenwu.Parameter Estimation of Stage-discharge Relationship Based on Machine Learning[J].Hydrology,2013,33(1):74-78. |
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| Authors: | JIANG Zhu SONG Wenwu |
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| Institution: | (School of Energy and Environment,Xihua University,Chengdu 610039,China) |
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| Abstract: | To overcome the limitation of the classical stage-discharge relationship model in describing the dynamic characteristics of a river,the locally weighted regression method was used to estimate the model parameters. In order to improve the estimation precision and the calcu -lation efficiency of river discharge, a novel method called clustering-tree weighted regression was proposed. Firstly, the trained samples wereclustered in this method. Secondly, k-nearest neighbors method was used to cluster new stage samples into the best fit clustering. Finally, thedaily discharge of the river was estimated. During the estimation process, the interference of irrelevant information was avoided, so the estima -tion precision and efficiency of daily discharge were improved. The data observed at some hydrological stations were used for the test. The sim -ulation results show that the estimation precision of this method is high. This provides a new effective method for the estimation of parameters ofstage-discharge relationship model. |
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| Keywords: | stage-discharge relationship parameter estimation locally weighted regression clustering tree k-nearest neighbors |
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