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

The application of artificial neural network (ANN) has gained significant interest while modeling various hydrologic processes. The main reason is the ANN models have produced promising results without the detailed information of watershed characteristics as required in physics based models. Still, the uncertainty in ANN models is a major issue that cannot be ignored. There could be different forms to represent model uncertainty, in which quantification of prediction interval for the model output has been mostly reported. In this paper, three different methods [i.e. Bootstrap method, Bayesian Approach and Prediction Interval (PI) method] were employed for quantifying the prediction interval in ANN models. The modeling procedure presented in this paper, is illustrated through river flow forecasting using the data collected from Kolar basin, India. The prediction interval was quantified using the measures such as percentage of coverage and average width. The comparison between these methods indicated that PI method has resulted in relatively less prediction and parameter uncertainty, besides the improved model performance. In addition, the PI method produced accurate prediction of hydrograph peak, which is a general concern in ANN models.