Machine learning approaches for estimation of prediction interval for the model output

@article{Shrestha2006MachineLA,
  title={Machine learning approaches for estimation of prediction interval for the model output},
  author={Durga L. Shrestha and Dimitri P. Solomatine},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2006},
  volume={19 2},
  pages={225-35}
}
A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 28 references

Machine learning in sedimentation modeling

C. Chatfield
Neural Networks • 2006

AdaBoost.RT: a boosting algorithm for regression problems

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) • 2004
View 1 Excerpt

Semi-optimal hierarchical regression models and ANNs

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) • 2004

Treatment of precipitation uncertainty in rainfall – runoff modelling

T. M. Mitchell
Advances in Water Resources • 2004

Model tree as an alternative to neural network in rainfallrunoff modelling

D. P. Solomatine, D. L. Shrestha
Hydrological Sciences Journal • 2003

The case for probabilistic forecasting in hydrology

R. McMillan. Krzysztofowicz
Journal of Hydrology • 2000
View 1 Excerpt

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