A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models: UNCERTAINTY ANALYSIS IN ANN HYDROLOGIC MODELS

@inproceedings{Srivastav2007ASA,
  title={A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models: UNCERTAINTY ANALYSIS IN ANN HYDROLOGIC MODELS},
  author={Roshan K. Srivastav and K. P. Sudheer and Indrajeet Chaubey},
  year={2007}
}
[1] One of the principal sources of uncertainty in hydrological models is the absence of understanding of the complex physical processes of the hydrological cycle within the system. This leads to uncertainty in input selection and consequently its associated parameters, and hence evaluation of uncertainty in a model becomes important. While there has been considerable interest in developing methods for uncertainty analysis of artificial neural network (ANN) models, most of the methods are… CONTINUE READING

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