Volume 6 Issue 2
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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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  • Behzad, M., Asghari, K., Eazi, M., and Palhang, M. 2009. Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with Applications, 36(4), 7624-7629. [doi: 10.1016/j.eswa.2008.09.053]
    Castro, R. M., Coates, M. J., and Nowak, R. D. 2004. Likelihood based hierarchical clustering. IEEE Transaction on Signal Process, 52(8), 2308-2321. [doi: 10.1109/TSP.2004.831124]
    Cleveland, W. S. 1979. Robust locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 74(368), 829-836.
    Cleveland, W. S., and Devlin, S. J. 1988. Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 596-610. [doi: 10.2307/2289282]
    Dai, L. Q., Dai, H. C., Jiang, D. G., Li, H., and Chen, X. Y. 2010. Calculation of stage-discharge relationship curve based on least square method. Yellow River, 32(9), 37-39. (in Chinese)
    Feng, G. Z., Wang, S. Y., and Wei, H. Y. 1996. Application of the multivariate autoregressive model to low flow forecast. Journal of Natural Resources, 11(2), 184-186. (in Chinese)
    Feng, H. Z., and Chen, Y. Y. 2004. A new method for non-linear classify and non-linear regression, II: Application of support vector machine to weather forecast. Journal of Applied Meteorological Science, 15(3), 355-365. (in Chinese)
    French, M. N., Krajewski, W. F., and Cuykendall, R. R. 1992. Rainfall forecasting in space and time using a neural network. Journal of Hydrology, 137(1-4), 1-31. [doi: 10.1016/0022-1694(92)90046-X]
    Lu, M. 2006. Research on the SVM application of runoff forecast. China Rural Water and Hydropower, (2), 47-49. (in Chinese)
    Mitchell, T. M. 2003. Machine Learning. Beijing: China Machine Press. (in Chinese)
    Shi, K. P., Mu, G., Li, T., and Lü, L. 2007. Empirical mode decomposition based clustering-tree method and its application in coherency identification of generating sets. Power System Technology, 31(22), 21-25. (in Chinese)
    Zhu, M. 2002. Data Mining. Hefei: University of Science and Technology of China Press. (in Chinese)
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