The moving confluence route technology with WAD scheme for 3D hydrodynamic simulation in high altitude inland waters

Wang, Yonggui; Yang, Yu-Jun; Chen, Xiaolong; Engel, Bernard A.; Zhang, Wanshun

doi:10.1016/j.jhydrol.2018.02.038

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…The ECOMSED contains five modules: hydrodynamic module, sediment transport module, wind induced wave module, heat flux module, and particle tracking module, which have functions for water levels, currents, temperature, salinity, tracers, cohesive and non-cohesive sediments, and waves simulation [12]. Recently, the ECOMSED system has been extensively used around the world and proven to be quite robust and reliable over the year [28,29]. The ECOMSED is used with a sigma coordinate system, which is with regular grids in orthogonal Cartesian coordinates in horizontal direction and sigma levels in the vertical direction.…”

Section: The Improvement Of Ecomsedmentioning

confidence: 99%

A Mesh Reduced Method for Speeding Up Structured Grid-Based Water Quantity and Quality Models in Large-Scale River Networks

Kang

Wang

et al. 2019

Water

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High-precision and efficiently distributed discrete element models for water environment simulation are urgently needed in large-scale river network areas, but most distributed discrete element models are serially computed and need performance improving. Parallel computing and other common methods for models’ high performance have large labor costs and are complicated. We put forward a new mesh reduced method for promoting computational efficiency with grid re-organization according to the structure and algorithm characteristics of 2D and 3D numerical models. This simple and cheap method was adapted to a classical three-dimensional hydrodynamic and sediment model (ECOMSED) for model improvement and effective evaluation. Six schemes with different grids were made to investigate the performance of this method in the river network area of the Three Gorges Reservoir Basin. It showed good characteristics of simulation performance and model speed-up. We concluded that the method is viable and efficient for optimizing distributed discrete element models.

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Section: The Improvement Of Ecomsedmentioning

confidence: 99%

A Mesh Reduced Method for Speeding Up Structured Grid-Based Water Quantity and Quality Models in Large-Scale River Networks

Kang

Wang

et al. 2019

Water

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“…Hydrodynamic modeling can usually simulate the hydrodynamics of an entire basin, even in areas where there are no measuring devices, with a satisfactory level of accuracy. Additionally, it requires fewer calibration points and can generate simulated hydrodynamic data for making predictions in unmeasured areas, which can provide a large amount of high-quality training data for deep learning [11,12]. Nevertheless, process-based models often encounter unfavorable factors, such as uncertainty in parameters, uncertainty in boundary conditions, and uncertainty in structure [13].…”

Section: Introductionmentioning

confidence: 99%

A Deep U-Net-ConvLSTM Framework with Hydrodynamic Model for Basin-Scale Hydrodynamic Prediction

Li,

Zhang,

Zhang

et al. 2024

Water

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Traditional hydrodynamic models face the significant challenge of balancing the demands of long prediction spans and precise boundary conditions, large computational areas, and low computational costs when attempting to rapidly and accurately predict the nonlinear spatial and temporal characteristics of fluids at the basin scale. To tackle this obstacle, this study constructed a novel deep learning framework with a hydrodynamic model for the rapid spatiotemporal prediction of hydrodynamics at the basin scale, named U-Net-ConvLSTM. A validated high-fidelity hydrodynamic mechanistic model was utilized to build a 20-year hydrodynamic indicator dataset of the middle and lower reaches of the Han River for the training and validation of U-Net-ConvLSTM. The findings indicate that the R2 value of the model surpassed 0.99 when comparing the single-step prediction results with the target values. Additionally, the required computing time fell by 62.08% compared with the hydrodynamic model. The ablation tests demonstrate that the U-Net-ConvLSTM framework outperforms other frameworks in terms of accuracy for basin-scale hydrodynamic prediction. In the multi-step-ahead prediction scenarios, the prediction interval increased from 1 day to 5 days, while consistently maintaining an R2 value above 0.7, which demonstrates the effectiveness of the model in the missing boundary conditions scenario. In summary, the U-Net-ConvLSTM framework is capable of making precise spatiotemporal predictions in hydrodynamics, which may be considered a high-performance computational solution for predicting hydrodynamics at the basin scale.

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The architecture and application of an automatic operational model system for basin scale water environment management and design making supporting

Wang

Zhang

et al. 2021

Journal of Environmental Management

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The moving confluence route technology with WAD scheme for 3D hydrodynamic simulation in high altitude inland waters

Cited by 3 publications

References 43 publications

A Mesh Reduced Method for Speeding Up Structured Grid-Based Water Quantity and Quality Models in Large-Scale River Networks

A Mesh Reduced Method for Speeding Up Structured Grid-Based Water Quantity and Quality Models in Large-Scale River Networks

A Deep U-Net-ConvLSTM Framework with Hydrodynamic Model for Basin-Scale Hydrodynamic Prediction

The architecture and application of an automatic operational model system for basin scale water environment management and design making supporting

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