Estimating uncertainty of streamflow simulation using Bayesian neural networks Academic Article uri icon


  • Recent studies have shown that Bayesian neural networks (BNNs) are powerful tools for providing reliable hydrologic prediction and quantifying the prediction uncertainty. The reasonable estimation of the prediction uncertainty, a valuable tool for decision making to address water resources management and design problems, is influenced by the techniques used to deal with different uncertainty sources. In this study, four types of BNNs with different treatments of the uncertainties related to parameters (neural network's weights) and model structures were applied for uncertainty estimation of streamflow simulation in two U.S. Department of Agriculture Agricultural Research Service watersheds (Little River Experimental Watershed in Georgia and Reynolds Creek Experimental Watershed in Idaho). An advanced Markov chain Monte Carlo algorithm, evolutionary Monte Carlo, was used to train the BNNs and to estimate uncertainty limits of streamflow simulation. The results obtained in these two case study watersheds show that the 95% uncertainty limits estimated by different types of BNNs are different from each other. The BNNs that only consider the parameter uncertainty with noninformative prior knowledge contain the least number of observed streamflow data in their 95% uncertainty bound. By considering variable model structure and informative prior knowledge, the BNNs can provide more reasonable quantification of the uncertainty of streamflow simulation. This study stresses the need for improving understanding and quantifying methods of different uncertainty sources for effective estimation of uncertainty of hydrologic simulation using BNNs. Copyright 2009 by the American Geophysical Union.

published proceedings


author list (cited authors)

  • Zhang, X., Liang, F., Srinivasan, R., & Van Liew, M.

citation count

  • 63

complete list of authors

  • Zhang, Xuesong||Liang, Faming||Srinivasan, Raghavan||Van Liew, Michael

publication date

  • February 2009