Preprints
https://doi.org/10.5194/esurf-2022-23
https://doi.org/10.5194/esurf-2022-23
 
23 May 2022
23 May 2022
Status: this preprint is currently under review for the journal ESurf.

Development of a machine learning model for river bedload

Hossein Hosseiny1, Claire Masteller1, and Colin Phillips2 Hossein Hosseiny et al.
  • 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.

Hossein Hosseiny et al.

Status: open (until 13 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esurf-2022-23', Anonymous Referee #1, 23 May 2022 reply
    • RC2: 'Reply on RC1', Basil Gomez, 25 May 2022 reply
  • CC1: 'Comment on esurf-2022-23', Xingyu Chen, 26 May 2022 reply
  • RC3: 'Comment on esurf-2022-23', Anonymous Referee #3, 26 May 2022 reply

Hossein Hosseiny et al.

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Short summary
It is of great importance to engineers and geomorphologists to predict the rate of bedload in rivers. In this contribution, we used a large dataset of measured data and developed an artificial neural network (ANN), a machine learning algorithm, for bedload prediction. The ANN model predicted the bedload flux close to measured values and better than the ones obtained from four standard bedload models with varying degrees of complexity.