Statistical models for improving significant wave height predictions in offshore operations

Emmanouil, Stergios; Aguilar, Sandra Gaytan; Nane, Gabriela F.; Schouten, Jan-Joost

doi:10.1016/j.oceaneng.2020.107249

Cited by 31 publications

(7 citation statements)

References 54 publications

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“…In the case of low complexity and few input variables, this method had high prediction accuracy. Emmanouil et al [77] improved the numerical prediction of SWH using a Bayesian network (BN). Using a special cyclic NN structure, the long short-term memory (LSTM; [78]) network can avoid the problem of long-term data dependence and is very suitable for wave height prediction problems.…”

Section: Oceanic Phenomena Forecastingmentioning

confidence: 99%

Recent Developments in Artificial Intelligence in Oceanography

Han

Bethel

et al. 2022

Ocean-Land-Atmos Res

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With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications. In this paper, these applications are reviewed from the perspectives of identifying, forecasting, and parameterizing ocean phenomena. Specifically, the usage of AI algorithms for the identification of mesoscale eddies, internal waves, oil spills, sea ice, and marine algae are discussed in this paper. Additionally, AI-based forecasting of surface waves, the El Niño Southern Oscillation, and storm surges is discussed. This is followed by a discussion on the usage of these schemes to parameterize oceanic turbulence and atmospheric moist physics. Moreover, physics-informed deep learning and neural networks are discussed within an oceanographic context, and further applications with ocean digital twins and physics-constrained AI algorithms are described. This review is meant to introduce beginners and experts in the marine sciences to AI methodologies and stimulate future research toward the usage of causality-adherent physics-informed neural networks and Fourier neural networks in oceanography.

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Section: Oceanic Phenomena Forecastingmentioning

confidence: 99%

Recent Developments in Artificial Intelligence in Oceanography

Han

Bethel

et al. 2022

Ocean-Land-Atmos Res

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“…Significant wave height, current and wind speed restrictions apply to all marine activities. Some are more sensitive to this, but generally it is important to overcome this restriction by precise weather monitoring and forecast (Emmanouil et al (2020)) or using innovative ships and technologies.…”

Section: Other Upcoming Projectsmentioning

confidence: 99%

Reply on RC2

Ramachandran

2021

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“…Because of the importance of accurately predicting SWH, researchers have conducted extensive research on SWH prediction methods. Traditional methods mainly include statistical methods and numerical simulation methods and have been widely used in global sea state prediction (Group, 1988;Vanem, 2016;Kazeminezhad and Siadatmousavi, 2017;Umesh and Swain, 2018;Liang et al, 2019;Liu et al, 2019;Swain et al, 2019;Emmanouil et al, 2020;Gao et al, 2020;Li et al, 2020;Gao et al, 2021a). Both statistical methods and numerical simulation methods attempt to predict SWH through approximate mathematical relational models.…”

Section: Introductionmentioning

confidence: 99%

Significant wave height prediction based on deep learning in the South China Sea

Hao

Gao

2023

Front. Mar. Sci.

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Significant wave height (SWH) prediction can effectively improve the safety of marine activities and reduce the occurrence of maritime accidents, which is of great significance to national security and the development of the marine economy. In this study, we comprehensively analyzed the SWH prediction performance of the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit network (GRU) by considering different input lengths, prediction lengths, and model complexity. The experimental results show that (1) the input length impacts the prediction results of SWH, but it does not mean that the longer the input length, the better the prediction performance. When the input length is 24h, the prediction performance of RNN, LSTM, and GRU models is better. (2) The prediction length influences the SWH prediction results. As the prediction length increases, the prediction performance gradually decreases. Among them, RNN is not suitable for 48h long-term SWH prediction. (3) The more layers of the model, the better the SWH prediction performance is not necessarily. When the number of layers is set to 3 or 4, the model’s prediction performance is better.

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Statistical models for improving significant wave height predictions in offshore operations

Cited by 31 publications

References 54 publications

Recent Developments in Artificial Intelligence in Oceanography

Recent Developments in Artificial Intelligence in Oceanography

Reply on RC2

Significant wave height prediction based on deep learning in the South China Sea

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