Articles | Volume 11, issue 4
Research article
27 Jul 2023
Research article |  | 27 Jul 2023

Development of a machine learning model for river bed load

Hossein Hosseiny, Claire C. Masteller, Jedidiah E. Dale, and Colin B. Phillips

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Cited articles

Ancey, C.: Stochastic modeling in sediment dynamics: Exner equation for planar bed incipient bed load transport conditions, J. Geophys. Res.-Earth, 115, 1–21,, 2010. 
Asheghi, R. and Hosseini, S. A.: Prediction of bed load sediments using different artificial neural network models, Front. Struct. Civ. Eng., 14, 374–386,, 2020. 
Ashida, K. and Michiue, M.: Hydraulic Resistance of Flow in an Alluvia Bed and Bed Load Transport Rate, Proc. JSCE, 206, 59–69, 1972 (in Japanese). 
Barry, J. J., Buffington, J. M., and King, J. G.: A general power equation for predicting bed load transport rates in gravel bed rivers, Water Resour. Res., 40, 1–22,, 2004. 
Barry, J. J., Buffington, J. M., Goodwin, P., King, J. G., and Emmett, W. W.: Performance of Bed-Load Transport Equations Relative to Geomorphic Significance: Predicting Effective Discharge and Its Transport Rate, Hydraul. Eng., 134, 601–615, 2008. 
Short summary
It is of great importance to engineers and geomorphologists to predict the rate of bed load 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 bed load prediction. The ANN model predicted the bed load flux close to measured values and better than the ones obtained from four standard bed load models with varying degrees of complexity.