• DocumentCode
    478137
  • Title

    Rainfall-Runoff Modeling at Daily Scale with Artificial Neural Networks

  • Author

    Xu, Qin ; Ren, Liliang ; Yu, Zhongbo ; Yang, Bang ; Wang, Guizuo

  • Author_Institution
    State Key Lab. of Hydrol.-Water Resources & Hydraulic Eng., Hohai Univ., Nanjing
  • Volume
    2
  • fYear
    2008
  • fDate
    18-20 Oct. 2008
  • Firstpage
    504
  • Lastpage
    508
  • Abstract
    The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates a back-propagation (BP) neural networks model and a distributed hydrologic model for rainfall-runoff modeling in the upper area of Huai River, China. Methodologies and techniques of the two models are presented in this paper and a comparison of the simulated results between them is also conducted. The simulated results of the BP model indicate a satisfactory performance in the daily-scale simulation. The conclusions also indicate that the ANN-hydrologic models can be considered as an alternate and practical tool for hydrologic simulations in hydrologic science domain.
  • Keywords
    backpropagation; neural nets; artificial neural networks; back-propagation; distributed hydrologic model; rainfall-runoff modeling; Artificial neural networks; Computational modeling; Computer networks; Demand forecasting; Laboratories; Mathematical model; Power system modeling; Predictive models; Rivers; Water resources; ANN; BTOPMC; rainfall-runoff modeling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2008. ICNC '08. Fourth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-0-7695-3304-9
  • Type

    conf

  • DOI
    10.1109/ICNC.2008.559
  • Filename
    4667046