Wave forecasting within a port using WAVEWATCH III and artificial neural networks

Zheng, Zhenjun; Ma, Xiaozhou; Huang, Xuegang; Ma, Yuxiang; Dong, Guohai

doi:10.1016/j.oceaneng.2022.111475

Cited by 13 publications

(5 citation statements)

References 47 publications

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“…파랑모델은 대기모델에서 예측한 해상풍을 사용하여 파랑 의 발달 및 전파과정을 수치모의하기 위해 개발되었다. 현재 는 스펙트럼 기반의 3세대 파랑모델이 현업 예보와 연구 분 야에 사용되고 있으며, 계산 알고리즘 개선 (Rogers et al, 2012;Zieger et al, 2015;Liu et al, 2019) 및 고성능컴퓨 팅 (Tolman, 2002;Li, 2022;Zheng et al, 2022)을 통해 지 속적으로 예측성능 및 정확도 그리고 계산속도가 향상되고 있 다. 나아가, 이종모형간의 결합모델을 통해 복잡한 실제 문제 에 대한 접근이 가능하게 되었다 (Fan et al, 2012;Chen et al, 2013;Theurich et al, 2016;Moghimi et al, 2020).…”

Section: 서 론unclassified

Comparative Analysis of Forecasting Accuracy and Model Performance for Development of Coastal Wave Forecasting System Based on Unstructured Grid

Roh¹,

Oh²,

Chang³

et al. 2022

J Korean Soc Coast Ocean Eng

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We develop a coastal wave forecasting system by using the unstructured grid based on sea wind data of Global Data Assimilation and Prediction System. The verification is performed to examine the performance and accuracy of the wave model. Since the conventional grid has limited wave forecasting on complex coastlines and bathymetry, the unstructured grid system is applied for precise numerical simulation, and applicability for operational support is evaluated. Both grid systems show similar prediction trends in offshore and coastal areas, and the difference in prediction errors according to the grid system is not large. In addition, the applicability of the operational wave forecasting system is confirmed by dramatically reducing the model execution time of the unstructured grid under the same conditions.

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Section: 서 론unclassified

Comparative Analysis of Forecasting Accuracy and Model Performance for Development of Coastal Wave Forecasting System Based on Unstructured Grid

Roh¹,

Oh²,

Chang³

et al. 2022

J Korean Soc Coast Ocean Eng

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“…In recent years, artificial intelligence (AI) techniques have been extensively developed and applied, in multiple fields, to computationally analyze, model, and infer robust performance patterns in different highly monitored physical and engineering processes. Specifically, different cases of AI techniques applied in harbor operability assessment, both in terms of wave agitation [38][39][40][41][42] and moored ship motions [32,33,36], can be found in the literature. Using inference models, relations between variables have been established, allowing conclusions to be deduced from an initial multivariate database.…”

Section: Of 21mentioning

confidence: 99%

“…However, as opposed to the good accuracy in the results, the high number of parameters to be established as well as their arrangement/structure definition is usually a complex task conditioning the robustness and generalization of the prediction tools. These techniques applied for harbor wave agitation estimation can be found in [40], based on an instrumental dataset; in [39], trained with a physical modeling dataset; in [38,41,42], with previously generated numerical modeling-based datasets. In the previously cited work [33], an ANN-based model for predicting the moored ship response is also created based on the same instrumental dataset created from several field campaigns in the Outer Port of Punta Langosteira.…”

Section: Of 21mentioning

confidence: 99%

A Semi-Supervised Machine Learning Model to Forecast Movements of Moored Vessels

Romano-Moreno

Tomás

Diaz-Hernandez

et al. 2022

JMSE

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The good performance of the port activities in terminals is mainly conditioned by the dynamic response of the moored ship system at a berth. An adequate definition of the highly multivariate processes involved in the response of a moored ship at a berth is crucial for an appropriate characterization of port operability. The availability of an efficient forecast system of the movements of moored ships is essential for the planning, performance, and safety of the development of port operations. In this paper, an inference model to predict moored ship motions, based on a semi-supervised Machine Learning methodology, is presented. A comparison with different supervised and unsupervised Machine Learning techniques, as well as with existing Deep Learning-based models for predicting moored ship motions, has been performed. The highest performance of the semi-supervised Machine Learning-based model has been obtained. Additionally, the influence of infragravity wave parameters introduced as predictor variables in the model has been analyzed and compared with the typical ocean waves, wind, and sea level as predictor variables. The prediction model has been developed and validated with an available dataset of measured data from field campaigns in the Outer Port of Punta Langosteira (A Coruña, Spain).

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“…Additionally, the presence of nearshore wave farms in the vicinity of seaports could help to mitigate damaging effects on coastal structures [46] and hazards such as erosion [47] and flooding [48]. Another possible positive outcome, which lies beyond the scope of this work and still requires extensive research, relates to the potential impacts of wave farm operation on harbour oscillations (induced by infragravity waves) that can create significant problems to ships moored inside the harbour [49][50][51] and cargo operations [52,53]. On the other hand, the main disadvantage of this approach are the potential disruptions with the large amount of maritime uses (navigation, fishing, aquaculture and recreational activities) that are concentrated in the vicinity of seaports, which could limit the sea-space availability for the installation of WECs [54].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach to Assessing the Wave Potential for the Energy Supply of Ports: A Case Study

Ramos

Giannini

Calheiros-Cabral

et al. 2022

JMSE

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In recent years, seaports have faced increasing pressure to transition towards a low-carbon and more sustainable energy model. In this context, the exploitation of the local wave energy resource may appear as a promising alternative. Therefore, the objective of this work is to present a methodology to select the best WEC-site combination to supply the energy demands of ports. To illustrate this methodology, the Port of Leixões (Portugal) is used as a case study. For the selection of wave energy sites in port areas, the methodology proposes a detailed spatial characterisation of both the wave resource and marine uses. For the area of study, having considered the main marine uses (sediment disposal, biodiversity, aquaculture, recreational and navigation), two exploitable wave energy sites (Areas I and II) with average annual energy resources of 24 and 17 kWm−1, respectively, were found. Next, the methodology proposes a techno-economic optimisation of WECs, based on the local wave conditions of Areas I and II, to minimise their associated Levelised Cost of Energy (LCoE). The results obtained confirm the effectiveness of the methodology, with the novel oCECO device, appearing as the most feasible option (with an LCoE of EUR 387.6/MWh) to exploit the wave potential in the surrounding areas of the port.

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Wave forecasting within a port using WAVEWATCH III and artificial neural networks

Cited by 13 publications

References 47 publications

Comparative Analysis of Forecasting Accuracy and Model Performance for Development of Coastal Wave Forecasting System Based on Unstructured Grid

Comparative Analysis of Forecasting Accuracy and Model Performance for Development of Coastal Wave Forecasting System Based on Unstructured Grid

A Semi-Supervised Machine Learning Model to Forecast Movements of Moored Vessels

An Integrated Approach to Assessing the Wave Potential for the Energy Supply of Ports: A Case Study

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