In Situ Estimation of Erosion Model Parameters Using an Advection‐Diffusion Model and Bayesian Inversion

Edge, W. C.; Rayson, Matthew D.; Jones, Nicole L.; Ivey, Gregory

doi:10.1029/2022ms003500

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…Zhang, Nielsen, Perrochet, and Jia (2021) used spectral analysis (SA) and bandpass filtering to distinguish the HA and LR components. One of the most promising ideas to estimate a group of sediment transport parameters is through the best fitting of measured SSC time series and those predicted by analytical (Lavelle et al., 1984; Zhang et al., 2024), numerical (Amoudry et al., 2014; Edge et al., 2023; Erikson et al., 2013; D. Wang et al., 2018; Yang & Hamrick, 2003), or conceptual models (Hill et al., 2003; Jago & Jones, 1998; Weeks et al., 1993).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model

Zhang,

Zhao,

et al. 2024

Water Resources Research

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In‐situ observations of hydrodynamics and suspended sediment concentrations (SSCs) were conducted on an abandoned lobe in the northern part of the modern Yellow River Delta, China. The SSC record at the site is found to be the superposition of a general trend (fast increase and slow decrease cycle) caused by storm waves (SubSSC1) and relatively smaller fluctuations caused by tidal currents (SubSSC2). Physically, this indicates that storm waves eroded the bottom sediments while tidal currents then re‐suspended and advected the suspended sediments in the study area. To further obtain the suspended sediment transport parameters, first, SubSSC1 is modeled with significant wave height which incorporates a “memory curve” to consider the remaining impacts of historical waves. It is detected that waves in the past 75 hr still influence the present SSC which is reasonable because 75 hr is roughly the typical duration of a normal storm. Second, SubSSC2 is modeled with tidal excursion and trigonometric functions with measured periodicities. Finally, some sediment transport parameters, for example, the background SSC, the horizontal SSC gradient, the tidal constituents that advect it, and their relative time lags are optimized from the best fits of the measured and modeled SSC time series. The proposed framework for model construction and parameter optimization can be extended to other sea areas for inferring sediment transport parameters from field SSC time series at a specific station.

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Section: Introductionmentioning

confidence: 99%

Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model

Zhang,

Zhao,

et al. 2024

Water Resources Research

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Solving the one dimensional vertical suspended sediment mixing equation with arbitrary eddy diffusivity profiles using temporal normalized physics-informed neural networks

Zhang,

Deng,

et al. 2024

Physics of Fluids

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Analytical solutions are practical tools in ocean engineering, but their derivation is often constrained by the complexities of the real world. This underscores the necessity for alternative approaches. In this study, the potential of Physics-Informed Neural Networks (PINN) for solving the one-dimensional vertical suspended sediment mixing (settling-diffusion) equation which involves simplified and arbitrary vertical Ds profiles is explored. A new approach of temporal Normalized Physics-Informed Neural Networks (T-NPINN), which normalizes the time component is proposed, and it achieves a remarkable accuracy (Mean Square Error of 10−5 and Relative Error Loss of 10−4). T-NPINN also proves its ability to handle the challenges posed by long-duration spatiotemporal models, which is a formidable task for conventional PINN methods. In addition, the T-NPINN is free of the limitations of numerical methods, e.g., the susceptibility to inaccuracies stemming from the discretization and approximations intrinsic to their algorithms, particularly evident within intricate and dynamic oceanic environments. The demonstrated accuracy and versatility of T-NPINN make it a compelling complement to numerical techniques, effectively bridging the gap between analytical and numerical approaches and enriching the toolkit available for oceanic research and engineering.

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In Situ Estimation of Erosion Model Parameters Using an Advection‐Diffusion Model and Bayesian Inversion

Cited by 2 publications

References 39 publications

Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model

Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model

Solving the one dimensional vertical suspended sediment mixing equation with arbitrary eddy diffusivity profiles using temporal normalized physics-informed neural networks

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