# 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} }

- Published 2007
DOI:10.1029/2006wr005352

[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

#### Citations

##### Publications citing this paper.

SHOWING 1-10 OF 27 CITATIONS

## Quantification of the predictive uncertainty of artificial neural network based river flow forecast models

VIEW 9 EXCERPTS

CITES RESULTS, METHODS & BACKGROUND

## Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models

VIEW 9 EXCERPTS

CITES BACKGROUND & METHODS

## Assessment of SWAT to Enable Development of Watershed Management Plans for Agricultural Dominated Systems under Data-Poor Conditions

VIEW 5 EXCERPTS

CITES BACKGROUND & METHODS

HIGHLY INFLUENCED

## An operational dynamical neuro-forecasting model for hydrological disasters

VIEW 1 EXCERPT

CITES BACKGROUND

## Genetic programming based monthly groundwater level forecast models with uncertainty quantification

VIEW 1 EXCERPT

CITES BACKGROUND

## Impact of complexity on daily and multi-step forecasting of streamflow with chaotic, stochastic, and black-box models

VIEW 1 EXCERPT

CITES BACKGROUND

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 39 REFERENCES

## Bayesian neural network for rainfall-runoff modeling: BAYESIAN NEURAL NETWORK FOR RAINFALL-RUNOFF

VIEW 5 EXCERPTS

HIGHLY INFLUENTIAL

## Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks

VIEW 6 EXCERPTS

HIGHLY INFLUENTIAL

## Neural Networks for Pattern Recognition

VIEW 5 EXCERPTS

HIGHLY INFLUENTIAL

## Statistical Method in Hydrology

VIEW 3 EXCERPTS

HIGHLY INFLUENTIAL

## An Introduction to the Bootstrap

VIEW 3 EXCERPTS

HIGHLY INFLUENTIAL