Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

Fazeres-Ferradosa, Tiago; Taveira-Pinto, Francisco; Vanem, Erik; Reis, Maria Teresa; Neves, Luciana das

doi:10.1177/0309524x18777323

Cited by 51 publications

(11 citation statements)

References 56 publications

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“…It gives reasonable results and may be used for the assessment of extreme inundations on a beach (freak runups). In addition, in future applications, the statistical analysis hereby provided might also be useful in the study of the wave run-up phenomenon in other applications, e.g., in structures placed in shallow water conditions [31,32].…”

Section: Discussionmentioning

confidence: 99%

Extreme Inundation Statistics on a Composite Beach

Abdalazeez

Didenkulova

Dutykh

et al. 2020

Water

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The runup of initial Gaussian narrow-banded and wide-banded wave fields and its statistical characteristics are investigated using direct numerical simulations, based on the nonlinear shallow water equations. The bathymetry consists of the section of a constant depth, which is matched with the beach of constant slope. To address different levels of nonlinearity, time series with five different significant wave heights are considered. The selected wave parameters allow for also seeing the effects of wave breaking on wave statistics. The total physical time of each simulated time-series is 1000 h (~360,000 wave periods). The statistics of calculated wave runup heights are discussed with respect to the wave nonlinearity, wave breaking and the bandwidth of the incoming wave field. The conditional Weibull distribution is suggested as a model for the description of extreme runup heights and the assessment of extreme inundations.

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Section: Discussionmentioning

confidence: 99%

Extreme Inundation Statistics on a Composite Beach

Abdalazeez

Didenkulova

Dutykh

et al. 2020

Water

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“…Surface gravity waves (hereinafter, waves) are crucial physical phenomena to be considered in ocean engineering and renewable energy [1][2][3], shipping [4,5], scour protection, offshore wind foundations and breakwaters [6,7], amongst other activities. As such, accurate forecasts of evolution are indispensable.…”

Section: Introductionmentioning

confidence: 99%

Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network

Zhou

Bethel

Sun

et al. 2021

JMSE

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Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations. Additionally, artificial neural networks, though significantly computationally cheaper, faster, and effective, also experience difficulties with nonlinearities in the wave generation and evolution processes. To solve both problems, this study employs and couples empirical mode decomposition (EMD) and a long short-term memory (LSTM) network in a joint model for significant wave height forecasting, a method widely used in wind speed forecasting, but not yet for wave heights. Following a comparative analysis, the results demonstrate that EMD-LSTM significantly outperforms LSTM at every forecast horizon (3, 6, 12, 24, 48, and 72 h), considerably improving forecasting accuracy, especially for forecasts exceeding 24 h. Additionally, EMD-LSTM responds faster than LSTM to large waves. An error analysis comparing LSTM and EMD-LSTM demonstrates that LSTM errors are more systematic. This study also identifies that LSTM is not able to adequately predict high-frequency significant wave height intrinsic mode functions, which leaves room for further improvements.

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“…Vanem et al [8] studied some applications of extreme value analysis of ocean waves that are believed to be of relevance for coastal and ocean engineering. Fazeres-Ferradosa et al [9] presented copula-based approach to obtain the joint cumulative distribution function of the significant wave heights and the up-crossing mean period.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Model for Estimating Damage from Wind Waves during Coastal Storms

Choo

Chun

Jeon

et al. 2021

Water

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In recent years, climate abnormalities have been observed globally. Consequently, the scale and size of natural disasters, such as typhoons, wind wave, heavy snow, downpours, and storms, have increased. However, compared to other disasters, predicting the timing, location and severity of damages associated with typhoons and other extreme wind wave events is difficult. Accurately predicting the damage extent can reduce the damage scale by facilitating a speedy response. Therefore, in this study, a model to estimate the cost of damages associated with wind waves and their impacts during coastal storms was developed for the Republic of Korea. The history of wind wave and typhoon damages for coastal areas in Korea was collected from the disaster annual report (1991–2020), and the damage cost was converted such that it reflected the inflation rate as in 2020. Furthermore, data on ocean meteorological factors were collected for the events of wind wave and typhoon damages. Using logistic and linear regression, a wind wave damage prediction model reflecting the coastal regional characteristics based on 74 regions nationwide was developed. This prediction model enabled damage forecasting and can be utilized for improving the law and policy in disaster management.

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Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

Cited by 51 publications

References 56 publications

Extreme Inundation Statistics on a Composite Beach

Extreme Inundation Statistics on a Composite Beach

Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network

A Predictive Model for Estimating Damage from Wind Waves during Coastal Storms

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