Surface wave attenuation by vegetation with the stem, root and canopy

He, Fei; Chen, Jie; Jiang, Changbo

doi:10.1016/j.coastaleng.2019.103509

Cited by 54 publications

(29 citation statements)

References 26 publications

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“…The ̃ parameter relies on complex physics (e.g., skin friction, pressure differences, swaying of vegetation), which in turn depend on the vegetation properties in relation to the hydraulic conditions 14 . Therefore, instead of determining the ̃ values a-priori, several studies have attempted to calibrate the ̃ values to measurements and relate them to the Reynolds number [27][28][29][30] or the Keulegan-Carpenter number KC 24,[31][32][33] .…”

Section: Implications Of Measurements For Wave-vegetation Modellingmentioning

confidence: 99%

Woods versus waves: Wave attenuation through non-uniform forests under extreme conditions

Wesenbeeck¹,

Wolters²,

Antolínez³

et al. 2021

Preprint

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Worldwide, communities are facing increasing flood risk, due to more frequent and intense hazards and rising exposure through more people living along coastlines and in flood plains. Nature-based Solutions (NbS), such as mangroves, and riparian forests, offer huge potential for adaptation and risk reduction. The capacity of trees and forests to attenuate waves and mitigate storm damages receives massive attention, especially after extreme storm events. However, application of forests in flood mitigation strategies remains limited to date, due to lack of real-scale measurements on the performance under extreme conditions. Experiments executed in a large-scale flume with a willow forest to dissipate waves show that trees are hardly damaged and strongly reduce wave and run-up heights, even when maximum wave heights are up to 2.5 meters. It was observed for the first time that the surface area of the tree canopy is most relevant for wave attenuation, but that the very flexible leaves hardly add to effectiveness. Overall, the study shows that forests can play a significant role in reducing wave heights and run-up under extreme conditions. Currently, this potential is hardly used but may result in considerable cost savings in levee designs.

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Section: Implications Of Measurements For Wave-vegetation Modellingmentioning

confidence: 99%

Woods versus waves: Wave attenuation through non-uniform forests under extreme conditions

Wesenbeeck¹,

Wolters²,

Antolínez³

et al. 2021

Preprint

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show abstract

“…The normally accepted dimensionless parameter for evaluating the performance of the vegetation is the transmission coefficient (Kt) 4,6 , which is the ratio of the transmitted wave height to the incident wave height 5,11 . Kt can range from 0 to 1, where 0 illustrates no transmission and 1 illustrates complete transmission.…”

Section: Transmission Coefficientmentioning

confidence: 99%

“…Layout and detailed geometric parameters of the three models (Fig. 2) were published in Gong et al 5 .…”

Section: Test and Training Datamentioning

confidence: 99%

“…A total of 30 dataset of Model 1 has been used for the prediction of the Kt using the GP, ANNs and MNLR models. Kt as the dependent variable, and the Ursell number (Ur), relative width (RB), relative height (α) and volume fraction (φ) as the independent variables.The same process as He et al 5 , the power exponential function as shown in Eq. ( 8) is obtained by using the MNLR method.…”

Section: Prediction Of Rigid Cylinder Modelmentioning

confidence: 99%

“…Multivariate non-linear regression (MNLR) analysis is the most traditional approaches to establish prediction formulas 5,6 . However, that requires active assumption of the objective function, which requires background knowledge about the relationship between input and output.…”

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confidence: 99%

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Prediction of Transmission Coefficients of Regular Wave Attenuation by Emergent Vegetation

Gong

Jie

Jiang

et al. 2021

Preprint

Self Cite

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Transmission coefficient (Kt) for wave attenuation by vegetation is essential parameter for predicting the wave height. In this paper, based on the experimental data of three kind of artificial vegetation model, genetic programming (GP), artificial neural networks (ANNs) and multivariate non-linear regression (MNLR) were used to analyze the dimensionless factors including Ursell number (Ur), relative width (RB) relative height (α) and volume fraction (φ). The proposed GP formulae were compared with MNLR and ANNs. The predictions of GP models were in good agreement with measured data, and outperformed MNLR equations. Otherwise, GP and ANNs were used to obtain the weight of each factor. The results can provide a reference for the artificial planting of the three plants.

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Physical Model Study on the Soft Option of Coastal Protection Works by Vegetation Meadow—A Review

Surakshitha,

Manu,

Rao

2023

Lecture Notes in Civil Engineering

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Surface wave attenuation by vegetation with the stem, root and canopy

Cited by 54 publications

References 26 publications

Woods versus waves: Wave attenuation through non-uniform forests under extreme conditions

Woods versus waves: Wave attenuation through non-uniform forests under extreme conditions

Prediction of Transmission Coefficients of Regular Wave Attenuation by Emergent Vegetation

Physical Model Study on the Soft Option of Coastal Protection Works by Vegetation Meadow—A Review

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