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Despite the wide applications of artificial neural networks (ANNs) in modeling hydro-climatic processes, quantification of the ANNs’ performance is a significant matter. Sustainable management of water resources requires information about the amount of uncertainty involved in the modeling results, which is a guide for proper decision making. Therefore, in recent years, uncertainty analysis of ANN modeling has attracted noticeable attention. Prediction intervals (PIs) are one of the prevalent tools for uncertainty quantification. This review paper has focused on the different techniques of PI development in the field of hydrology and climatology modeling. The implementation of each method was discussed, and their pros and cons were investigated. In addition, some suggestions are provided for future studies. This review paper was prepared via PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology.

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In this paper, the point prediction of the Artificial Neural Network (ANN) for the suspended sediment load modeling was evaluated for the Lighvanchai River located in Iran, in monthly and daily scales. Since point prediction of ANN convey no information about the accuracy of prediction, so prediction intervals (PIs) were constructed by the Bootstrap method as a most frequently used technique for assessing the uncertainty of ANN. In this way, the accuracy of PIs was quantified by coverage and width criteria. The results showed that the ANN-based modeling in daily scale had better performance compared to that in monthly scale and Nash Sutcliff efficiency was 32% higher in daily scale compared to monthly. Moreover, the width and coverage of the constructed PIs in daily scale were 14% and 24%, lower and higher compared to that in monthly scale and the Bootstrap method could appropriately capture the target values.