Variation of dominant discharge along the riverbed based on numerical and deep-learning models: A case study in the Middle Huaihe River, China

Xu, Jianhong; Zhang, Chengxiao; Wang, Lingling; Zhu, Hai; Tang, Hongwu; Avital, Eldad

doi:10.1016/j.jhydrol.2022.128285

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…In the realm of hydraulics, ML is increasingly used to elevate the computational efficiency of predictive models. Various algorithms, from artificial neural networks (ANNs) (Campolo et al, 1999; Elsafi, 2014; Machado et al, 2011; Rigos et al, 2020) to support vector machines (SVMs) (Liong & Sivapragasam, 2002; Wu et al, 2008) and long short‐term memory models (LSTMs) (Le & Lee, 2019; Xu, Zhang, et al, 2022), have been successfully deployed for river flow and water level forecasting. These algorithms capably handle complex input–output relationships, demonstrating the versatility of ML in hydraulic applications.…”

Section: Introductionmentioning

confidence: 99%

Enhanced physics‐informed neural networks for efficient modelling of hydrodynamics in river networks

Luo,

Yuan,

Tang

et al. 2024

Hydrological Processes

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This article enhances the physics‐informed neural networks (PINNs) method to effectively model the hydrodynamics of real‐world river networks with irregular cross‐sections. First, we pre‐process hydraulic parameters to optimize training speed without compromising accuracy, achieving a 91.67% acceleration compared with traditional methods. To address the vanishing gradient problem, layer normalization is also incorporated into the architecture. We also introduce novel physical constraints—water level range and junction node equations—to ensure effective training convergence and enrich the model with additional physical insights. Two practical case studies using HEC‐RAS benchmarks demonstrate that our improved PINN method can predict river network hydrodynamics with less data and is less sensitive to time step size, allowing for longer computational time steps. Incorporating physical knowledge, our enhanced PINN methodology emerges as an efficient and promising avenue for modelling the complexities of hydrodynamic processes in natural river networks.

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Section: Introductionmentioning

confidence: 99%

Enhanced physics‐informed neural networks for efficient modelling of hydrodynamics in river networks

Luo,

Yuan,

Tang

et al. 2024

Hydrological Processes

Self Cite

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“…In addition, some methods are based on the optimization of certain parameters, such as the use of a formula for calculating the bed load sediment transport rate 18 , the selection of representative periods, and the index in the MK method 36 . Certain methods are based on the distribution characteristics of flow frequency curves 11 , numerical simulation, and deep learning models 37 .…”

Section: Introductionmentioning

confidence: 99%

Channel-forming discharge based on the extreme value identification of sediment-carrying capacity index

Ge,

Zhu

2024

Sci Rep

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Channel-forming discharge (Dcf) is an important parameter in river management and reservoir flood regulation. Applying the methods for calculating Dcf to reaches downstream reservoirs characterized by drastic changes in water and sediment conditions and long-term scouring status is difficult. Based on the riverbed-shaping principle of sediment-laden water flow, while simultaneously considering the active action of water flow and response of the riverbed, this study proposes a new method for calculating Dcf by identifying the extreme value of the suspended sediment-carrying capacity index. The application of this method to the middle and lower reaches of the Yangtze River showed that after the impoundment of the Three Gorges Reservoir, Dcf in this section was reduced by an amplitude between 2500 and 4700 m3/s. The results can be used to guide the operation of the Three Gorges Reservoir and the management of the middle and lower reaches of the Yangtze River, thus providing reference for other river channels downstream of the reservoir.

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Retrieval of suspended sediment concentrations using remote sensing and machine learning methods: A case study of the lower Yellow River

Hu,

Miao,

Zhang

et al. 2023

Journal of Hydrology

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Variation of dominant discharge along the riverbed based on numerical and deep-learning models: A case study in the Middle Huaihe River, China

Cited by 3 publications

References 42 publications

Enhanced physics‐informed neural networks for efficient modelling of hydrodynamics in river networks

Enhanced physics‐informed neural networks for efficient modelling of hydrodynamics in river networks

Channel-forming discharge based on the extreme value identification of sediment-carrying capacity index

Retrieval of suspended sediment concentrations using remote sensing and machine learning methods: A case study of the lower Yellow River

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