Volume 6 Issue 2
Apr.  2013
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Zhu JIANG, Hui-yan WANG, Wen-wu SONG. 2013: Discharge estimation based on machine learning. Water Science and Engineering, 6(2): 145-152. doi: 10.3882/j.issn.1674-2370.2013.02.003
Citation: Zhu JIANG, Hui-yan WANG, Wen-wu SONG. 2013: Discharge estimation based on machine learning. Water Science and Engineering, 6(2): 145-152. doi: 10.3882/j.issn.1674-2370.2013.02.003
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Discharge estimation based on machine learning

doi: 10.3882/j.issn.1674-2370.2013.02.003

  • School of Energy and Environment, Xihua University, Chengdu 610039, P. R. China

Funds:  This work was supported by the Key Fund Project of the Sichuan Provincial Department of Education (Grant No. 11ZA009), the Fund Project of Sichuan Provincial Key Laboratory of Fluid Machinery (Grant No. SBZDPY-11-5), and the Key Scientific Research Project of Xihua University (Grant No. Z1120413).
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  • Corresponding author: Zhu JIANG
  • Received Date: 2011-12-05
  • Rev Recd Date: 2012-06-09
    Abstract
  • To overcome the limitations of the traditional stage-discharge models in describing the dynamic characteristics of a river, a machine learning method of non-parametric regression, the locally weighted regression method was used to estimate discharge. With the purpose of improving the precision and efficiency of river discharge estimation, a novel machine learning method is proposed: the clustering-tree weighted regression method. First, the training instances are clustered. Second, the k-nearest neighbor method is used to cluster new stage samples into the best-fit cluster. Finally, the daily discharge is estimated. In the estimation process, the interference of irrelevant information can be avoided, so that the precision and efficiency of daily discharge estimation are improved. Observed data from the Luding Hydrological Station were used for testing. The simulation results demonstrate that the precision of this method is high. This provides a new effective method for discharge estimation.

     

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