Field Observation on Wave Set-up Near the Shoreline

Yanagishima, Shin-ichi; Katoh, Kenichi

doi:10.1061/9780872627765.009

Cited by 6 publications

(8 citation statements)

References 3 publications

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“…Similar results confirming the role of wave height, beach slope, and wave steepness on maximum setups were found by Yanagishima and Katoh (1990) and by O'Grady et al (2019). O'Grady et al (2019) tested different empirical equations and identified that deep water wave height explains 30% of setup variance, followed by an improvement of up to 12% if beach slope is added to the relationship and a further 12% when including wave steepness.…”

Section: Introductionsupporting

confidence: 81%

“…After this event, many laboratory experiments and field measurements have been conducted using Eq. ( 2) as the initial point to predict setup across the surf zone (Bowen et al, 1968;Battjes, 1974;Guza and Thornton, 1981;Holman and Sallenger Jr, 1985;King et al, 1990;Yanagishima and Katoh, 1990;Hanslow and Nielsen, 1993;Raubenheimer et al, 2001;Stockdon et al, 2006;Ji et al, 2018;O'Grady et al, 2019). As a result, empirical setup predictors based on wave parameters, beach morphology, and surf zone processes have been established (Dean and Walton, 2009;Gomes da Silva et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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A predictive equation for wave setup using genetic programming

Dalinghaus

Coco

Higuera

2022

Preprint

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Abstract. We applied machine learning to improve the accuracy of present predictors of wave setup. Namely, we used an evolutionary-based genetic programming model and a previously published dataset, which includes various beach and wave conditions. Here, we present two new wave setup predictors, a simple predictor, which is a function of wave height, wavelength, and beach slope, and a fitter, but more complex predictor, which is also a function of sediment diameter. The results show that the new predictors outperform existing formulas. Therefore, we conclude that machine learning models are capable of not only improving prediction capability (when compared to classical predictors) but also of providing physically sound descriptions of the processes modelled.

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Section: Introductionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

A predictive equation for wave setup using genetic programming

Dalinghaus

Coco

Higuera

2022

Preprint

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“…In this analysis, we defined the ground elevation of the shoreline position as the sum total of the mean astronomical tide level (D.L. +0.70 m) and the averaged value of the wave setup height near the shoreline (0.30 m), which is estimated by the empirical equation proposed by Yanagishima and Katoh [1990].…”

Section: Beach Profile Datamentioning

confidence: 99%

The Effects of Offshore Wave Energy Flux and Longshore Current Velocity on Medium-Term Shoreline Change at Hasaki, Japan

Suzuki

Kuriyama

2014

Coastal Engineering Journal

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The relationships between medium-term shoreline fluctuations, offshore wave energy flux and longshore current velocity were investigated on the basis of data sets from 1987 to 2001 (15 years). In this analysis, the spectral density of the data was calculated, and the frequency components from the investigated period (15 years) to 1000 days were recomposed and defined as the medium-term fluctuation. A multiple linear regression analysis was performed to evaluate the associations between the medium-term fluctuations of shoreline change rate and that of the major dependent variables, which are the offshore wave energy flux and the longshore current velocity. The results suggested that a medium-term fluctuation of the shoreline change rate at the field was affected by both the medium-term fluctuations of the offshore wave energy flux and the longshore current velocity at approximately the same rate. The fluctuation range of the medium-term shoreline fluctuation was nearly the same range of the seasonal shoreline fluctuation, i.e. approximately 20 m. This indicates that for beach management, not only seasonal shoreline fluctuation but also medium-term shoreline fluctuation is needed to be considered.

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“…Further, for a dissipative beach (mild slope) the wave setup was not dependent on the surf similarity (ξ) parameter (Guza and Thornton 1981;Hanslow and Nielsen 1993). On intermediate to reflective beaches however, the surf similarity was shown to be a governing factor in determining the wave setup (Bowen, Inman, and Simmons 1968;Holman and Sallenger 1985;Yanagishima and Katoh 1990;Stockdon et al 2006). The other results were from studies in a lake environment (Greenwood and Osborne 1990), setup at the shoreline (Davis and Nielsen 1988), or returned low values when compared to analytical results (King et al 1990; Dean and Walton 2010) with low values in some cases for dissipative beaches (Stockdon et al 2006).…”

Section: Wave Setupmentioning

confidence: 99%

“…Many of these studies show that the wave setup is dependent on the deep water heights (Bowen, Inman, and Simmons 1968;Hansen 1978;Guza and Thornton 1981;Holman and Sallenger 1985;Davis and Nielsen 1988;King et al 1990;Greenwood and Osborne 1990;Yanagishima and Katoh 1990). Aside from the 1968 study by Bowen, Inman and Simmons, all of the other relationships were derived for natural beaches.…”

Section: Wave Setupmentioning

confidence: 99%

Towards a Probabilistic Flood Base Model for Caribbean Coasts

Subrath-Ali¹,

Villarroel-Lamb²,

Kelman³

2012

Int. Conf. Coastal. Eng.

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An estimate of the total water level setup at the coast is examined at a monthly resolution to assess the base combined contribution from wind, wave and pressure setup in the absence of cyclonic activity. A stochastic approach is used to estimate the total values of water level setup from the three parameters and the results indicate that while the total coastal water level setup closely follows the wave setup due to a difference of an order of magnitude of two with the wind and pressure setup from the inverse barometer effect, the latter two parameters have a greater impact during the first seven months of the year – the dry season with an approximate two month overlap into the rainy season. Using the generated moments from monthly combined probability density functions, we show that the average coastal setup from the three driving forces show a general decreasing trend from the highest coastal setup months of December, January and February.

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Field Observation on Wave Set-up Near the Shoreline

Cited by 6 publications

References 3 publications

A predictive equation for wave setup using genetic programming

A predictive equation for wave setup using genetic programming

The Effects of Offshore Wave Energy Flux and Longshore Current Velocity on Medium-Term Shoreline Change at Hasaki, Japan

Towards a Probabilistic Flood Base Model for Caribbean Coasts

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