Improved streamflow forecasting using self-organizing radial basis function artificial neural networks

Hamid Moradkhani, Kuo Lin Hsu, Hoshin V. Gupta, Soroosh Sorooshian

Research output: Contribution to journalArticlepeer-review

182 Scopus citations


Streamflow forecasting has always been a challenging task for water resources engineers and managers and a major component of water resources system control. In this study, we explore the applicability of a Self Organizing Radial Basis (SORB) function to one-step ahead forecasting of daily streamflow. SORB uses a Gaussian Radial Basis Function architecture in conjunction with the Self-Organizing Feature Map (SOFM) used in data classification. SORB outperforms the two other ANN algorithms, the well known Multi-layer Feedforward Network (MFN) and Self-Organizing Linear Output map (SOLO) neural network for simulation of daily streamflow in the semi-arid Salt River basin. The applicability of the linear regression model was also investigated and concluded that the regression model is not reliable for this study. To generalize the model and derive a robust parameter set, cross-validation is applied and its outcome is compared with the split sample test. Cross-validation justifies the validity of the nonlinear relationship set up between input and output data.

Original languageEnglish (US)
Pages (from-to)246-262
Number of pages17
JournalJournal of Hydrology
Issue number1-4
StatePublished - Aug 10 2004


  • Cross-validation
  • Neural network
  • Radial basis function
  • Self-organizing feature map
  • Streamflow forecasting

ASJC Scopus subject areas

  • Water Science and Technology


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