Dear Editor

Paper is interesting and well-written, but my main issue is regarding the lack of novelty. Authors just applied old model of ANN to bedload prediction. We have many new models like tree-based, rule based,... deep learning and so on. Due to lack of novelty, I have to reject the paper. Sorry for the decision.

Best

The authors use an ANN to derive a nonlinear relationship between several river hydraulic variables and sediment transport rate. This machine learning technique and many others have been extensively tested in the last twenty years in many similar studies that the authors overlooked. Thus, I do not think that this study adds something new to the existing literature and I suggest rejection of the paper.

In this short and well-written manuscript, the authors present an ANN model for predicting bedload flux based on a published dataset. Machine learning is increasingly used for modeling and predicting natural dynamics, with known strengths and limitations. Bedload is perhaps one of the more challenging processes to model given its strong dependency on highly dynamic and local variables. A number of models have recently been published that attempt to predict bedload over large scales (continental and global; see below). This paper is therefore quite timely and adds to the broader communities' efforts to better predict fluvial dynamics. The following issues should be addressed before it is accepted for publication. These are not very major issues but will likely require additional analysis.

1. The observational dataset includes an unequal number of observations for each river - if the spatial variability is larger than the temporal variability this may lead to overfitting. The authors addressed that to a degree, but need to better discuss this issue. As it stands the model predicts temporal dynamics using observations from different rivers. ANN may be flexible enough to deal with this but, again, needs more discussion and maybe an additional analysis using some sort of average value for each site (regression may be more suitable in this case given the small sample size).

2. The removal of outliers is overall acceptable but can be very problematic when using a fluvial dataset as the 'extreme' values are often just the few large rivers in a dataset. The authors warn the reader to only use/interpret the results within the range of the variables but they should more carefully examine the outliers and try to include realistic observations and maximize the dataset (and thus model) representation of large rivers.

3. The metrics selected for representing the models' accuracy are reasonable but need some justification. Why MSE and not RMSE or PBIAS or R2?

4. The paper falls short in providing tools and guidelines for applying its outcomes. The paper's main outcome is to demonstrate the potential usefulness of ANN for modeling bedload flux. How can the reader use this knowledge moving forward? Will they have to develop their own ANN based on the dataset? How can it be used for other locations (as the authors suggested)? This is a common issue with ML modeling, but the authors can mitigate it with additional descriptions and tools (e.g. scripts). 

5. The authors are encouraged to explore recently published papers such as:

Cohen, S., Syvitski, J., Ashely, T., Lammers, R., Fekete, B., & Li, H. Y. (2022). Spatial Trends and Drivers of Bedload and Suspended Sediment Fluxes in Global Rivers. Water Resources Research, e2021WR031583.