Data driven analysis on the extreme wave statistics over an area

Tang, Tianning; Adcock, Thomas A. A.

doi:10.1016/j.apor.2021.102809

Cited by 4 publications

(3 citation statements)

References 49 publications

(74 reference statements)

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“…The second order contribution can be included by replacing the linear distribution with the second order distribution curve in Eq. ( 24), which can be obtained by either second order simulations based on the free wave component (Trulsen and Dysthe, 1996;Slunyaev, 2005;Slunyaev et al, 2013) (see also (Dalzell, 1999)) or statistical models (Benetazzo et al, 2015;Fedele et al, 2017;Forristall, 2015;Tang and Adcock, 2021).…”

Section: Resultsmentioning

confidence: 99%

A reduced order model for space–time wave statistics using probabilistic decomposition–synthesis method

Tang

Adcock²

2022

Ocean Engineering

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Section: Resultsmentioning

confidence: 99%

A reduced order model for space–time wave statistics using probabilistic decomposition–synthesis method

Tang

Adcock²

2022

Ocean Engineering

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“…Machine learning algorithms can establish relationships between the input and output features through supervised learning. Tang and Adcock [41] and Adnan, et al [42] predicted sea surface elevation data for short-term wave analysis using random forest models. Some artificial neural network (ANN) models, such as backpropagation (BP) ANN [43][44][45] and long short-term memory (LSTM) ANN [46], are also applied to preprocess, calibrate, or forecast the data used for long-term wave analysis.…”

Section: Data Availabilitymentioning

confidence: 99%

Developments in Modeling Techniques for Reliability Design of Aquaculture Cages: A Review

Wang,

Ma,

Chu

et al. 2024

JMSE

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Offshore aquaculture is gaining traction due to space limitations in nearshore waters, more pristine water, cooler temperatures, and better waste dispersal. This move has spurred the development of new technologies for offshore aquaculture. Despite the numerous analysis methods for designing aquaculture infrastructure, limitations and challenges remain in modeling the influence of fish cages on flow fields and in addressing fluid–structure interaction. This paper presents a comprehensive review of analysis methods and modeling techniques applied in the design of offshore aquaculture systems, emphasizing the structural reliability analysis. This review includes statistical and predictive analysis of extreme sea conditions, evaluation of environmental loads and hydrodynamic analysis, structural reliability modeling and assessment, and seabed geotechnical responses to mooring anchors. For each design consideration, the relevant theories and applicability are elaborated upon and discussed. This review provides valuable insights for engineers involved in the development and design of offshore aquaculture infrastructure.

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“…Mahjoobi et al [22] used the regression tree for wave height prediction and found that the error statistics of the predicted results were within the allowance range. Tang et al [32] explored the maximum wave crest distribution using random forest and pointed out that its accuracy was higher than traditional numerical models. Although the regression tree could deal with discrete and continuous variables, fewer requirements for data, and minor sensitivity to outliers, it was prone to overfitting especially for highdimensional data [29].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Occurrence Probability of Freak Waves in Unidirectional Sea State Using Deep Learning

Zhou,

Wang,

Ding

et al. 2023

JMSE

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Predicting extreme waves can foresee the hydrodynamic environment of marine engineering, critical for avoiding disaster risks. Till now, there are barely any available models that can rapidly and accurately predict the occurrence probability of freak waves in a given state. This paper develops a trained model based on the Back Propagation (BP) neural network, with wave parameters of unidirectional sea state fed into the model, such as significant wave height, wave period, spectral type, and the intermodal distance of the peak frequencies. A rapid and accurate model optimized for predicting the occurrence probability of freak waves in a unidirectional sea state, from unimodal to bimodal configuration, is achieved by iterating to reduce accumulation errors. Compared to the regression and least-squares boosting trees, the optimized model performs much better in accurately predicting the occurrence probability of freak waves. Irrespective of whether in unimodal or bimodal sea state, this optimized model is competitive in calculation accuracy compared to theoretical models such as Rayleigh prediction and MER prediction, improved by at least 41%. The established model based on the BP neural network can quickly predict the threshold of freak waves in a given sea state, guiding practical engineering applications.

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Data driven analysis on the extreme wave statistics over an area

Cited by 4 publications

References 49 publications

A reduced order model for space–time wave statistics using probabilistic decomposition–synthesis method

A reduced order model for space–time wave statistics using probabilistic decomposition–synthesis method

Developments in Modeling Techniques for Reliability Design of Aquaculture Cages: A Review

Prediction of the Occurrence Probability of Freak Waves in Unidirectional Sea State Using Deep Learning

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