Streamflow forecasting with uncertainty estimate using Bayesian learning for ANN

Accurate site-specific streamflow forecasts along with uncertainty estimate are of particular importance for water resources planning and management. In the last decade, different types of artificial neural network (ANN) models have been shown as promising alternative methods for rainfall-runoff modeling. However, one of the critical issues with ANN based modeling remains the lack of confidence limits for the prediction results. Therefore, whatever the accuracy of the prediction values, there is a lack of reliability for practical applications. The Bayesian learning algorithm overcomes that limitation by providing uncertainty estimates of the predicted results. The present paper introduces a Bayesian learning approach for ANN modeling of daily streamflows implemented with a multilayer perceptron (MLP). The proposed model results are compared with those obtained from a multilayer perceptron trained with a 'scaled conjugate gradient' method. Overall, the model validation statistics and hydrograph comparison indicate that the Bayesian learning approach outperforms the conventional approach in almost all respects.