Hillsborough bay inflow modification study: An application of the Tampa Bay Coastal Ocean Model

Chen, Jing; Weisberg, Robert H.; Liu, Yonggang; Zheng, Lianyuan

doi:10.1016/j.ecss.2023.108213

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…TBCOM is used to assess the responses of the Tampa Bay estuary to either naturally occurring (e.g. Beck et al, 2022;Chen et al 2018bChen et al , 2020Liu et al, 2017Liu et al, , 2022Liu et al, , 2023 or anthropogenic (Liu et al, 2021;Chen et al 2023a) perturbations. The TBCOM hindcast products also have been used to support the studies of an anchored spar-buoy seafloor geodetic system at the mouth of Tampa Bay (Xie et al 2019(Xie et al , 2021.…”

Section: Tampa Bay Coastal Ocean Modelmentioning

confidence: 99%

Hydrodynamic response to bathymetric changes in Tampa Bay, Florida

Chen,

Liu,

Weisberg

et al. 2023

Deep Sea Research Part II: Topical Studies in Oceanography

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Section: Tampa Bay Coastal Ocean Modelmentioning

confidence: 99%

Hydrodynamic response to bathymetric changes in Tampa Bay, Florida

Chen,

Liu,

Weisberg

et al. 2023

Deep Sea Research Part II: Topical Studies in Oceanography

Self Cite

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“…The watershed model assumes a pivotal role in the calculation of river discharge using conventional hydrodynamic principles. This approach offers the capacity to encompass a designated regional area and furnish precise data on water flow characteristics, alongside key water quality parameters, such as EFDC (Environmental Fluid Dynamics Code) [2][3][4]. Nevertheless, these numerical simulation methods necessitate a lot of effort in grid generation, and the computational process is time consuming.…”

Section: Introductionmentioning

confidence: 99%

Computing River Discharge Using Water Surface Elevation Based on Deep Learning Networks

Liu,

Zou,

Jiang

et al. 2023

Water

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Accurately computing river discharge is crucial, but traditional computing methods are complex and need the assistance of many other hydraulic parameters. Therefore, it is of practical value to develop a convenient and effective auto-computation technique for river discharge. Water surface elevation is relatively easy to obtain and there is a strong relationship between river discharge and water surface elevation, which can be used to compute river discharge. Unlike previous usage of deep learning to predict short-term river discharge that need multiple parameters besides water level, this paper proved that deep learning has the potential to accurately compute long-term river discharge purely based on water level. It showed that the majority of relative errors on the test dataset were within ±5%, particularly it could operate continuously for almost one year with high precision without retraining. Then, we used BiGRU to compute river flow with different hyperparameters, and its best RMSE, NSE, MAE, and MAPE values were 256 m3/s, 0.9973, 207 m3/s, and 0.0336, respectively. With this data-driven based technology, it will be more convenient to obtain river discharge time series directly from local water surface elevation time series accurately in natural rivers, which is of practical value to water resources management and flood protection.

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