Development of a machine learning model for river bedload
- 1Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA
- 2Department of Civil and Environmental Engineering, Utah State University, Logan, UT, USA
- 1Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA
- 2Department of Civil and Environmental Engineering, Utah State University, Logan, UT, USA
Abstract. Prediction of bedload sediment transport rates in rivers is a notoriously challenging problem due to inherent variability in river hydraulics and channel morphology. Machine learning offers a compelling approach to leverage the growing wealth of bedload transport observations towards the development of a data driven predictive model. We present an artificial neural network (ANN) model for predicting bedload transport rates informed by 8,117 measurements from 134 rivers. Inputs to the model were river discharge, flow width, bed slope, and four bed surface sediment sizes. A sensitivity analysis showed that all inputs to the ANN model contributed to a reasonable estimate of bedload flux. At individual sites, the ANN model was able to reproduce observed sediment rating curves with a variety of shapes and outperformed four standard bedload models. This ANN model has the potential to be broadly applied to predict bedload fluxes based on discharge and reach properties alone.
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Hossein Hosseiny et al.
Status: open (until 13 Jul 2022)
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RC1: 'Comment on esurf-2022-23', Anonymous Referee #1, 23 May 2022
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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
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RC2: 'Reply on RC1', Basil Gomez, 25 May 2022
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My review is contained in the attached pdf
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RC2: 'Reply on RC1', Basil Gomez, 25 May 2022
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CC1: 'Comment on esurf-2022-23', Xingyu Chen, 26 May 2022
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The paper is well-written. A highligh of the study is the use of large dataset. The value of the study can be better addressed if the author better explore how the increase of the dataset size (from 500 to 8000+) can help improve the understanding of bedload transport. There must be a lot of information contained in the 8000+ bedload measurements that can be helpful in explaining the variability of bedload transport.
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RC3: 'Comment on esurf-2022-23', Anonymous Referee #3, 26 May 2022
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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.
Hossein Hosseiny et al.
Data sets
Bedload data set Hossein Hosseiny, Claire Masteller, Colin Phillips https://docs.google.com/spreadsheets/d/1TeGFcRfFqCaD-8keugCDIBftpl0Mxpz5/edit?usp=sharing&ouid=113425085155864679118&rtpof=true&sd=true
Hossein Hosseiny et al.
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