WAM and WAVEWATCH-III intercomparison studies in the North Indian Ocean using Oceansat-2 Scatterometer winds

Swain, J.; Umesh, P. A.; Balchand, A. N.

doi:10.1177/2516019219866569

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…where r t is the real observation of the significant wave height at time t and nt is computed according to (14) and obtained as follows: , we train the N-LSTM by maximizing the log-likelihood as follows:…”

Section: N-lstm Trainingmentioning

confidence: 99%

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Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory

Zhang

Gao

et al. 2021

JMSE

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This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical predictions into long short-term memory (LSTM). Specifically, a novel ocean wave height series prediction framework, referred to as numerical long short-term memory (N-LSTM), is introduced. The N-LSTM takes a combined wave height representation, which is formed of a current wave height measurement and a subsequent Simulating Waves Nearshore (SWAN) numerical prediction, as the input and generates the corrected numerical prediction as the output. The correction is achieved by two modules in cascade, i.e., the LSTM module and the Gaussian approximation module. The LSTM module characterizes the correlation between measurement and numerical prediction. The Gaussian approximation module models the conditional probabilistic distribution of the wave height given the learned LSTM. The corrected numerical prediction is obtained by sampling the conditional probabilistic distribution and the corrected numerical prediction series is obtained by iterating the N-LSTM. Experimental results validate that our N-LSTM effectively lifts the accuracy of wave height numerical prediction from SWAN for the Bohai Sea and Xiaomaidao. Furthermore, compared with the state-of-the-art machine learning based prediction methods (e.g., residual learning), the N-LSTM achieves better prediction accuracy by 10% to 20% for the prediction time varying from 3 to 72 h.

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Section: N-lstm Trainingmentioning

confidence: 99%

“…The WAM model and WMIII model are similar in structure. WMIII uses more complicated dissipation source terms and wind input terms than the WAM model [14]. Liu et al [15] use the data of the South Indian Ocean to compare the performance of WAM and WMIII.…”

Section: Introductionmentioning

confidence: 99%

Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory

Zhang

Gao

et al. 2021

JMSE

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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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“…Therefore, the prediction of SWH has always been a matter of special concern. Under normal circumstances, the prediction can be completed by numerical models such as WAM [6][7][8], WAVEWATCH [9][10][11], SWAN [12][13][14]. However, because the strong nonlinear physical process and mechanism of ocean waves are still unclear, the numerical model is still unable to obtain high accuracy to a large extent [15].…”

Section: Introductionmentioning

confidence: 99%

CLTS-Net: A More Accurate and Universal Method for the Long-Term Prediction of Significant Wave Height

Hao

et al. 2021

JMSE

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Significant wave height (SWH) prediction plays an important role in marine engineering areas such as fishery, exploration, power generation, and ocean transportation. For long-term forecasting of a specific location, classical numerical model wave height forecasting methods often require detailed climatic data and incur considerable calculation costs, which are often impractical in emergencies. In addition, how to capture and use the dynamic correlation between multiple variables is also a major research challenge for multivariate SWH prediction. To explore a new method for predicting SWH, this paper proposes a deep neural network model for multivariate time series SWH prediction—namely, CLTS-Net. In this study, the sea surface wind and wave height in the ERA5 dataset of the relevant points P1, P2, and P3 from 2011 to 2018 were used as input information to train the model and evaluate the model’s SWH prediction performance. The results show that the correlation coefficients (R) of CLTS-Net are 0.99 and 0.99, respectively, in the 24 h and 48 h SWH forecasts at point P1 along the coast. Compared with the current mainstream artificial intelligence-based SWH solutions, it is much higher than ANN (0.79, 0.70), RNN (0.82, 0.83), LSTM (0.93, 0.91), and Bi-LSTM (0.95, 0.94). Point P3 is located in the deep sea. In the 24 h and 48 h SWH forecasts, the R of CLTS-Net is 0.97 and 0.98, respectively, which are much higher than ANN (0.71, 0.72), RNN (0.85, 0.78), LSTM (0.85, 0.78), and Bi-LSTM (0.93, 0.93). Especially in the 72 h SWH forecast, when other methods have too large errors and have lost their practical application value, the R of CLTS-Net at P1, P2, and P3 can still reach 0.81, 0.71, and 0.98. The results also show that CLTS-Net can capture the short-term and long-term dependencies of data, so as to accurately predict long-term SWH, and has wide applicability in different sea areas.

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WAM and WAVEWATCH-III intercomparison studies in the North Indian Ocean using Oceansat-2 Scatterometer winds

Cited by 8 publications

References 34 publications

Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory

Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory

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

CLTS-Net: A More Accurate and Universal Method for the Long-Term Prediction of Significant Wave Height

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