Integration of data-driven and physics-based modeling of wind waves in a shallow estuary

Wang, Nan; Chen, Qin; Zhu, Ling; Sun, Hao

doi:10.1016/j.ocemod.2022.101978

Cited by 17 publications

(2 citation statements)

References 28 publications

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“…Such a method enables the hindcast of long‐term wave climate in an estuary. Details about the training, validation, and testing of the ANN can be found in Wang, Chen, and Chen (2022); Wang, Chen, Zhu, and Sun (2022). The calibration and validation of the SWAN model for wave hindcast in Terrebonne Bay was carried out using the 8‐month wave measurements at the study site from January to August in 2012 (Everett et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Near‐continuous monitoring of a coastal salt marsh margin: Implications for predicting marsh edge erosion

Cadigan

Jafari

Wang

et al. 2023

Earth Surf Processes Landf

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Mechanisms that control marsh edge erosion include wind‐generated waves, vegetation productivity, land use and land change, and geotechnical properties of sediments. However, existing models for predicting marsh edge evolution focus primarily on edge retreat rates as a function of wave energy while accounting for other controlling factors as empirical constants. This simplification arises from a lack of high‐frequency monitoring of marsh evolutions. In particular, marsh erosion is timescale dependent, and conducting field observations on short temporal and spatial scales could elucidate the progression of erosion, which may improve marsh erosion predictive models. This study developed and validated a near‐continuous camera and erosion pin monitoring system to document marsh edge erosion at a high frequency (i.e., daily) in Terrebonne Bay, Louisiana. This was supplemented with daily wave power to explore the relationships between daily erosion and wave power. Long‐term average erosion rates derived from satellite and aerial imagery from 1989 through 2019 compare similarly to rates derived from longer‐term site visits (i.e., monthly) at approximately 2.2 m/yr. High‐magnitude erosion events (>20 cm/day) are driven by a buildup in wave energy over a 7‐day time period coupled with a strong 1‐day wave event, indicating a gradual reduction in marsh edge resistance with continued wave attack. Long‐term erosion monitoring methods, including monthly field visits, provide results that align well with previously reported relationships between wave power and erosion. High‐frequency measurements, however, illustrate that the previously published trends smooth over the large‐magnitude short‐term erosion events, potentially obscuring the physical processes of marsh edge erosion. For example, satellite and aerial imagery provide a long period of record, but they may underestimate the average annual erosion rate in the region, the effect of which may become exasperated over the varying temporal scales considered in coastal planning efforts across the USA and worldwide.

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Section: Methodsmentioning

confidence: 99%

Near‐continuous monitoring of a coastal salt marsh margin: Implications for predicting marsh edge erosion

Cadigan

Jafari

Wang

et al. 2023

Earth Surf Processes Landf

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“…In the study However, the results of this model in this study and previous research indicate that the SMB semi-analytical model could not provide a reliable estimate of wave height, particularly in coastal areas and shallower waters. This model is better suited for initial estimations and small environments where local effects are negligible [78].…”

Section: Sverdrup Munk Bretschneidermentioning

confidence: 99%

Novel Ocean Wave Height and Energy Spectrum Forecasting Approaches: An Application of Semi-Analytical and Machine Learning Models

Elkhrachy,

Alhamami,

Alyami

et al. 2023

Water

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Accurate and reliable wave forecasting is crucial for optimizing the performance of various marine operations, such as offshore energy production, shipping, and fishing. Meanwhile, predicting wave height and wave energy is crucial for achieving sustainability as a renewable energy source, as it enables the harnessing of the power of wave energy efficiently based on the water-energy nexus. Advanced wave forecasting models, such as machine learning models and the semi-analytical approach, have been developed to provide more accurate predictions of ocean waves. In this study, the Sverdrup Munk Bretschneider (SMB) semi-analytical approach, Emotional Artificial Neural Network (EANN) approach, and Wavelet Artificial Neural Network (WANN) approach will be used to estimate ocean wave parameters in the Gulf of Mexico and Aleutian Basin. The accuracy and reliability of these approaches will be evaluated, and the spatial and temporal variability of the wave field will be investigated. The available wave characteristics are used to generate hourly, 12-hourly, and daily datasets. The WANN and SMB model shows good performance in the daily prediction of the significant wave height in both case studies. In the SMB model, specifically on a daily time scale, the Nash–Sutcliffe Efficiency (NSE) and the peak deviation coefficient (DCpeak) were determined to be 0.62 and 0.54 for the Aleutian buoy and 0.64 and 0.55 for the Gulf of Mexico buoy, respectively, for significant wave height. In the context of the WANN model and in the testing phase at the daily time scale, the NSE and DCpeak indices exhibit values of 0.85 and 0.61 for the Aleutian buoy and 0.72 and 0.61 for the Gulf of Mexico buoy, respectively, while the EANN model is a strong tool in hourly wave height prediction (Aleutian buoy (NSEEANN = 0.60 and DCpeakEANN = 0.88), Gulf of Mexico buoy (NSEEANN = 0.80 and DCpeakEANN = 0.82)). In addition, the findings pertaining to the energy spectrum density demonstrate that the EANN model exhibits superior performance in comparison to the WANN and SMB models, particularly with regard to accurately estimating the peak of the spectrum (Aleutian buoy (DCpeakEANN= 0.41), Gulf of Mexico buoy (DCpeakEANN = 0.59)).

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Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments

Zhang

Zhao

et al. 2023

Applied Energy

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Integration of data-driven and physics-based modeling of wind waves in a shallow estuary

Cited by 17 publications

References 28 publications

Near‐continuous monitoring of a coastal salt marsh margin: Implications for predicting marsh edge erosion

Near‐continuous monitoring of a coastal salt marsh margin: Implications for predicting marsh edge erosion

Novel Ocean Wave Height and Energy Spectrum Forecasting Approaches: An Application of Semi-Analytical and Machine Learning Models

Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments

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