CMIP5-Based Wave Energy Projection: Case Studies of the South China Sea and the East China Sea

Zheng, Chunmiao; Wu, Guoxiang; Chen, Xuan; Wang, Qing; Gao, Zhan-sheng; Chen, Yun-Ge; Luo, Xia

doi:10.1109/access.2019.2924197

Cited by 27 publications

(8 citation statements)

References 28 publications

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Section: Predictor Selectionmentioning

confidence: 98%

“…It is necessary to reject poorly relative predictors. (2) The correlations between different predictors are also remarkable. Taking strongly relative predictors as input variables simultaneously could result in information redundancy, which increases the cost and running time of a prediction system, as well as degrades its generalization performance.…”

Section: Predictor Selectionmentioning

confidence: 98%

“…Wave height prediction is important for planning offshore engineering, such as exploitation of marine renewable energy, harbor constructions, and marine operations. Besides studies on the long-term wave height prediction [1,2], real-time and fast forecasting also plays a crucial role in offshore operations, which has received considerable critical attention.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Prediction of Significant Wave Height Using Dynamic Bayesian Network and Information Flow

Ming

Liu

2020

Water

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Short-term prediction of wave height is paramount in oceanic operation-related activities. Statistical models have advantages in short-term wave prediction as complex physical process is substantially simplified. However, previous statistical models have no consideration in selection of predictive variables and dealing with prediction uncertainty. This paper develops a machine learning model by combining the dynamic Bayesian network (DBN) with the information flow (IF) designated as DBN-IF. IF is focused on selecting the best predictive variables for DBN by causal analysis instead of correlation analysis. DBN for probabilistic prediction is constructed by structure learning and parameter learning with data mining. Based on causal theory, graph theory, and probability theory, the proposed DBN-IF model could deal with the uncertainty and shows great performance in significant wave height prediction compared with the artificial neural network (ANN), random forest (RF) and support vector machine (SVM) for all lead times. The interpretable DBN-IF is proven as a promising tool for nonlinear and uncertain wave height prediction.

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Section: Predictor Selectionmentioning

confidence: 98%

Section: Predictor Selectionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Probabilistic Prediction of Significant Wave Height Using Dynamic Bayesian Network and Information Flow

Ming

Liu

2020

Water

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“…In this study, the WPE is estimated by Equation (2) using the 2D wave spectrum in each model output step. The WPE can also be estimated from the integrated parameters when the 2D wave spectrum is not available [36].…”

Section: Energy Potential Parametersmentioning

confidence: 99%

Impact of Air–Wave–Sea Coupling on the Simulation of Offshore Wind and Wave Energy Potentials

Shao

2020

Atmosphere

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Offshore wind and wave energy potentials are commonly simulated by atmosphere and wave stand-alone models, in which the Atmosphere–Wave–Ocean (AWO) dynamical coupling processes are neglected. Based on four experiments (simulated by UU-CM, Uppsala University-Coupled model) with four different coupling configurations between atmosphere, waves, and ocean, we found that the simulations of the wind power density (WPD) and wave potential energy (WPE) are sensitive to the AWO interaction processes over the North and Baltic Seas; in particular, to the atmosphere–ocean coupling processes. Adding all coupling processes can change more than 25% of the WPE but only less than 5% of the WPD in four chosen coastal areas. The impact of the AWO coupling processes on the WPE and WPD changes significantly with the distance off the shoreline, and the influences vary with regions. From the simulations used in this study, we conclude that the AWO coupling processes should be considered in the simulation of WPE and WPD.

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“…There is a positive correlation when swh is less than 0.5m and mwd is greater than 200 • in Region A, and when swh is greater than 1m and mwd is greater than 200 • in Region B and C. The agitation of wind waves would release the hidden nutrients on the seabed and bring them to the surface [38], [39]. The wind and wave would also make the phytoplankton in the surface layer move with the wind direction and change the geographical position, furthermore, bringing new species and biomass to some seas [40], [41]. These comprehensive factors increased the Chl-a along the coast.…”

Section: ) Impact Of Environmental Factors On the Variation Of Chl-amentioning

confidence: 99%

Temporal-Spatial Distribution of Chlorophyll-a and Impacts of Environmental Factors in the Bohai Sea and Yellow Sea

Zhao

Zhang

et al. 2019

IEEE Access

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Better understanding of the temporal-spatial distribution of chlorophyll-a concentration (Chl-a) is crucial in controlling harmful water blooms. In this study, the dynamical change of Chl-a over the Bohai Sea and Yellow Sea from 2003-2017 were analyzed by using the MODIS/Aqua satellite data, and the effects of sea surface temperature (SST), wind and wave were investigated. The typical distribution modes of long-term surface Chl-a were extracted by using the Self-organizing Mapping (SOM), neural network model. The results showed distinct seasonal variations of the Chl-a along with a gradual increase in the study period. The total Chl-a of the whole area reached the lowest value of 2.41mg/m 3 in July, and the highest value 3.43mg/m 3 in April; though in Laizhou Bay, the Chl-a concentration was significantly higher than other regions and the value reached at the peak in September. The spatial distribution showed that Chl-a decreased from inshore to offshore. Meanwhile, from clear mode to low, medium, and high concentration modes, the Chl-a gradually increased in coverage and concentration, and modes extracted by the SOM neural network have effectively elucidated the trend of Chl-a in spatial, seasonal, and interannual variability. The Generalized Additive Model (GAM) was used to evaluate the effect of SST, wind, and wave on the changing patterns of Chl-a. It was found that there is a significant nonlinear correlation between Chl-a and SST, wind speed, mean wave direction and significant height of the wave. These influencing factors accounted for 47.9% of the change of Chl-a, which had significant effects on Chl-a change. Compared with wind speed, mean wave direction and significant height of wave, SST can better explain the change of Chl-a. Besides, wind direction and increased human activity (e.g., river discharge) played a significant role in changing the Chl-a distribution in the Bohai Sea and Yellow Sea. INDEX TERMS Chl-a, self-organizing mapping neural network, generalized additive model, environmental factors, the Bohai sea and Yellow sea.

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CMIP5-Based Wave Energy Projection: Case Studies of the South China Sea and the East China Sea

Cited by 27 publications

References 28 publications

Probabilistic Prediction of Significant Wave Height Using Dynamic Bayesian Network and Information Flow

Probabilistic Prediction of Significant Wave Height Using Dynamic Bayesian Network and Information Flow

Impact of Air–Wave–Sea Coupling on the Simulation of Offshore Wind and Wave Energy Potentials

Temporal-Spatial Distribution of Chlorophyll-a and Impacts of Environmental Factors in the Bohai Sea and Yellow Sea

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