Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?

Khabat Khosravia School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, CanadaCorrespondence[email protected]
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Aitazaz A. Farooquea School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, CanadaView further author information

Sayed M. Batenib Department of Civil, Environmental and Construction Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, USAView further author information

Changhyun Junc Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Seoul, Republic of KoreaCorrespondence[email protected]
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Dorsa Mohammadid Earth & Environmental Sciences Department, Boston University, Boston, MA, USAView further author information

Zahra Kalantarie Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH Royal Institute of Technology, Stockholm, SwedenView further author information

James R. Cooperf Department of Geography & Planning, School of Environmental Sciences, University of Liverpool, Liverpool, UKView further author information

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Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?

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Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?

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