Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze , China 619

@inproceedings{Dawson2002EvaluationOA,
  title={Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze , China 619},
  author={Christian W. Dawson and C. Harpham and Robert L. Wilby and Yun-Feng Chen},
  year={2002}
}
While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam) for the period 1991 to 1993, it is shown that both… CONTINUE READING
19 Citations
21 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 19 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 21 references

Inferring conceptual rainfall–runoff model processes using artificial neural networks

  • R. L. Wilby, R. Abrahart, C. W. Dawson
  • Water Resour. Res.,
  • 2002
2 Excerpts

How well does your model fit the data

  • M. J. Hall
  • J. Hydroinformatics,
  • 2001
2 Excerpts

Inferring conceptual rainfall-runoff model processes with artificial neural networks. European Geophys.Soc

  • R. J. Abrahart, R. L. Wilby, C. W. Dawson
  • General Assembly,
  • 2001
3 Excerpts

River flow prediction using artificial neural networks: generalisation beyond the calibration range

  • C. E. Imrie, S. Durucan, A. Korre
  • J. Hydrol.,
  • 2000
1 Excerpt

Similar Papers

Loading similar papers…