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?

Khosravi, Khabat (author): School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, Canada

Farooque, Aitazaz A. (author): School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, Canada

Bateni, Sayed M. (author): Department of Civil, Environmental and Construction Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, USA

Jun, Changhyun (author): Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Seoul, Republic of Korea

Mohammadi, Dorsa (author): Earth & Environmental Sciences Department, Boston University, Boston, MA, USA

Kalantari, Zahra, 1979- (author): KTH,Vatten- och miljöteknik

Cooper, James R. (author): Department of Geography & Planning, School of Environmental Sciences, University of Liverpool, Liverpool, UK

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Informa UK Limited, 2024
2024
English.
In: Engineering Applications of Computational Fluid Mechanics. - : Informa UK Limited. - 1994-2060 .- 1997-003X. ; 18:1

Related links:: https://doi.org/10.1...; show more...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

The potential of advanced tree-based models and optimized deep learning algorithms to predict fluvial bedload transport was explored, identifying the most flexible and accurate algorithm, and the optimum set of readily available and reliable inputs. Using 926 datasets for 20 rivers, the performance of three groups of models was tested: (1) standalone tree-based models Alternating Model Tree (AMT) and Dual Perturb and Combine Tree (DPCT); (2) ensemble tree-based models Iterative Absolute Error Regression (IAER), ensembled with AMT and DPCT; and (3) optimized deep learning models Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) ensembled with Grey Wolf Optimizer. Comparison of the predictive performance of the models with that of commonly used empirical equations and sensitivity analysis of the driving variables revealed that: (i) the coarse grain-size percentile D90 was the most effective variable in bedload transport prediction (where Dx is the xth percentile of the bed surface grain size distribution), followed by D84, D50, flow discharge, D16, and channel slope and width; (ii) all tree-based models and optimized deep learning algorithms displayed ‘very good’ or ‘good’ performance, outperforming empirical equations; and (iii) all algorithms performed best when all input parameters were used. Thus, a range of different input variable combinations must be considered in the optimization of these models. Overall, ensemble algorithms provided more accurate predictions of bedload transport than their standalone counterpart. In particular, the ensemble tree-based model IAER-AMT performed most strongly, displaying great potential to produce robust predictions of bedload transport in coarse-grained rivers based on a few readily available flow and channel variables.

Subject headings

NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Keyword

Bedload sediment
deep learning
Einstein (1950)
empirical equations
IAER-AMT
machine learning

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ref (subject category)
art (subject category)

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By the author/editor: Khosravi, Khabat; Farooque, Aitaza ...; Bateni, Sayed M.; Jun, Changhyun; Mohammadi, Dorsa; Kalantari, Zahra ...; show more...; Cooper, James R.; show less...

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NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Vision ...

Articles in the publication: Engineering Appl ...

By the university: Royal Institute of Technology

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