Previous studies have shown that the predictive ability of artificial neural networks can be improved by combining multiple models. This study presents a jittering-based neural network ensemble approach to obtain improved regional low flow estimates at ungauged sites. The fundamental idea of the jittered ensemble is to add noises to the explanatory variables and thereby augments the training data sets to develop the network models based on different but associated training data. To evaluate the performance of the jittered ensemble, this study employs two different neural network architectures, a single-output neural network model and a multi-output neural network model. In addition, a jittered ensemble approach is coupled with the variable importance measuring algorithm to infer the relationship between basin characteristics and predicted low flow quantiles. Effectiveness of the proposed methods is demonstrated using selected basins in the northeastern United States. Results suggest that the jittering-based ensemble model is able to consistently outperform a single modeling approach. Also, improvement is achieved even using small sizes of ensembles although a sufficient sample size offers more reliable predictions. Finally, this study recognizes that the effects of the basin characteristics varied or remained constant based on the low flow quantiles considered.
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