Sequential Monte Carlo (SMC) methods are known to be very effective for the state and parameter estimation of nonlinear and non-Gaussian systems. In this study, SMC is applied to the parameter estimation of an artificial neural network (ANN) model for streamflow prediction of a watershed. Through SMC simulation, the probability distribution of model parameters and streamflow estimation is calculated. The results also showed the SMC approach is capable of providing reliable streamflow prediction under limited available observations.
Research Article|April 02 2010
Hydrologic forecasting using artificial neural networks: a Bayesian sequential Monte Carlo approach
1Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California Irvine, Irvine CA 92697-2175, USA.
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Kuo-Lin Hsu; Hydrologic forecasting using artificial neural networks: a Bayesian sequential Monte Carlo approach. Journal of Hydroinformatics 1 January 2011; 13 (1): 25–35. doi: https://doi.org/10.2166/hydro.2010.044
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