Predicting Equilibrium Bed Morphology of Large‐Scale Meandering Rivers Using a Novel LES‐Trained Machine Learning Approach.
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| Title: | Predicting Equilibrium Bed Morphology of Large‐Scale Meandering Rivers Using a Novel LES‐Trained Machine Learning Approach. |
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| Authors: | Zhang, Zexia1 (AUTHOR), Gholami Anjiraki, Mehrshad1 (AUTHOR), Seyedzadeh, Hossein1 (AUTHOR), Sotiropoulos, Fotis2 (AUTHOR), Yang, Xiaolei3 (AUTHOR), Khosronejad, Ali1 (AUTHOR) ali.khosronejad@stonybrook.edu |
| Source: | Journal of Advances in Modeling Earth Systems. Oct2025, Vol. 17 Issue 10, p1-29. 29p. |
| Subject Terms: | *Streamflow, *Sediment transport, Meandering rivers, Machine learning, Prediction models, Sedimentology, Large eddy simulation models |
| Abstract: | Flood‐induced deformation of the bed topography of fluvial meandering rivers could lead to riverbank displacement, infrastructure failure, and the propagation of scour and deposition features. The ability to predict sediment transport in large‐scale meanders is, therefore, a critical requirement for tackling a host of environmental issues. High‐fidelity simulations of large‐scale rivers coupling large‐eddy simulation (LES) of the flow with morphodynamic processes can provide accurate predictions of such phenomena, but this approach is computationally very expensive owing to the costly two‐way coupling between turbulence and bed morphodynamics across disparate time scales. This study presents a novel machine learning approach trained on data from high‐fidelity simulations. We develop and demonstrate the performance of a convolutional neural network autoencoder (CNNAE) algorithm to generate high‐fidelity bed shear stress and equilibrium morphology of large‐scale meandering rivers. The CNNAE algorithm utilizes as input instantaneous shear stress distribution and change of bed elevation obtained from high‐fidelity simulation results, along with geometric parameters of meanders to predict mean bed shear stress distribution and equilibrium bed elevation of rivers. The proposed machine learning approach predicts bed shear stress and equilibrium bed morphology of large‐scale meanders under bankfull flow conditions at a fraction of the cost of brute‐force coupled LES‐morphodynamics. Plain Language Summary: It is vital to understand the physics of flood flow in natural rivers. Such understanding can help practicing engineers, researchers, and stakeholders to appropriately design infrastructures along and across rivers and better protect the river environment. To understand the flood flow and its impact on rivers, this study develops and utilizes artificial intelligence (AI)‐based machine‐learning algorithms to predict bed shear stress and bed topography of large‐scale under flood conditions. The developed AI algorithms enable reliable predictions of flood flow and bed topography at a fraction of the computational cost of existing models. Thus, the findings of this study can benefit society by providing stakeholders with AI‐based modeling tools to predict flood impacts on the stability of infrastructure in natural waterways and evaluate the efficiency of flood mitigation strategies. Key Points: A machine learning framework is proposed to predict mean bed shear stress and equilibrium bed topography of large‐scale meandering riversThe proposed artificial intelligence (AI) method accurately captures meandering rivers' scour and deposition regions and bed shear distribution with bankfull flowsThe computational cost of simulations using the proposed AI framework is less than 2% of coupled large‐eddy simulation and morphodynamics modeling [ABSTRACT FROM AUTHOR] |
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| Abstract: | Flood‐induced deformation of the bed topography of fluvial meandering rivers could lead to riverbank displacement, infrastructure failure, and the propagation of scour and deposition features. The ability to predict sediment transport in large‐scale meanders is, therefore, a critical requirement for tackling a host of environmental issues. High‐fidelity simulations of large‐scale rivers coupling large‐eddy simulation (LES) of the flow with morphodynamic processes can provide accurate predictions of such phenomena, but this approach is computationally very expensive owing to the costly two‐way coupling between turbulence and bed morphodynamics across disparate time scales. This study presents a novel machine learning approach trained on data from high‐fidelity simulations. We develop and demonstrate the performance of a convolutional neural network autoencoder (CNNAE) algorithm to generate high‐fidelity bed shear stress and equilibrium morphology of large‐scale meandering rivers. The CNNAE algorithm utilizes as input instantaneous shear stress distribution and change of bed elevation obtained from high‐fidelity simulation results, along with geometric parameters of meanders to predict mean bed shear stress distribution and equilibrium bed elevation of rivers. The proposed machine learning approach predicts bed shear stress and equilibrium bed morphology of large‐scale meanders under bankfull flow conditions at a fraction of the cost of brute‐force coupled LES‐morphodynamics. Plain Language Summary: It is vital to understand the physics of flood flow in natural rivers. Such understanding can help practicing engineers, researchers, and stakeholders to appropriately design infrastructures along and across rivers and better protect the river environment. To understand the flood flow and its impact on rivers, this study develops and utilizes artificial intelligence (AI)‐based machine‐learning algorithms to predict bed shear stress and bed topography of large‐scale under flood conditions. The developed AI algorithms enable reliable predictions of flood flow and bed topography at a fraction of the computational cost of existing models. Thus, the findings of this study can benefit society by providing stakeholders with AI‐based modeling tools to predict flood impacts on the stability of infrastructure in natural waterways and evaluate the efficiency of flood mitigation strategies. Key Points: A machine learning framework is proposed to predict mean bed shear stress and equilibrium bed topography of large‐scale meandering riversThe proposed artificial intelligence (AI) method accurately captures meandering rivers' scour and deposition regions and bed shear distribution with bankfull flowsThe computational cost of simulations using the proposed AI framework is less than 2% of coupled large‐eddy simulation and morphodynamics modeling [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19422466 |
| DOI: | 10.1029/2024MS004710 |
