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www.hydrol-earth-syst-sci-discuss.net/6/1677/2009/
doi:10.5194/hessd-6-1677-2009
© Author(s) 2009. This work is distributed
under the Creative Commons Attribution 3.0 License.
A novel approach to parameter uncertainty analysis of hydrological models using neural networks
1UNESCO-IHE Institute for Water Education, Delft, The Netherlands
2MULTI Disciplinary Consultants Ltd, Kathmandu, Nepal
3Water Resources Section, Delft University of Technology, The Netherlands
Abstract. In this study, a methodology has been developed to replicate time consuming Monte Carlo (MC) simulation by using an Artificial Neural Network (ANN) for assessment of model parametric uncertainty. First, MC simulation of a given process model is run. Then an ANN is trained to approximate the functional relationships between the input variables of the process model and the synthetic uncertainty descriptors estimated from the realizations. The trained ANN model encapsulates the underlying characteristics of the parameter uncertainty and can be used to predict uncertainty descriptors for the new data vectors. This approach was validated by comparing the uncertainty descriptors in the verification data set with those obtained by MC simulation. The method is applied to estimate parameter uncertainty of a lumped conceptual hydrological model, HBV, for the Brue catchment in UK. The results are quite promising as the prediction intervals estimated by ANN are reasonably accurate. The proposed techniques could be useful in real time applications when it is not practicable to run a large number of simulations for complex hydrological models and when the forecast lead time is very short.
Discussion Paper (PDF, 1913 KB) Interactive Discussion (Closed, 6 Comments) Final Revised Paper (HESS)
Citation: Shrestha, D. L., Kayastha, N., and Solomatine, D. P.: A novel approach to parameter uncertainty analysis of hydrological models using neural networks, Hydrol. Earth Syst. Sci. Discuss., 6, 1677-1706, doi:10.5194/hessd-6-1677-2009, 2009. Bibtex EndNote Reference Manager XML
