Wave estimation within a port using a fully nonlinear Boussinesq wave model and artificial neural networks

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

doi:10.1016/j.oceaneng.2020.108073

Cited by 34 publications

(15 citation statements)

References 44 publications

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“…The Boussinesq-type (BT) models have been widely used to analyse wave propagation in port environments (e.g. Woo and Liu, 2004;Belloti, 2007;Belloti and Franco, 2011;Thotagamuwage and Pattiaratchi, 2014;Zheng et al, 2020;Malej et al, 2021). For this reason, in this case a BT model (LIMPORT, developed by the research team, Sierra et al, 1988) was used with a variable spatial resolution between 3x3 m and 5x5 m, depending on port size.…”

Section: Figurementioning

confidence: 99%

Impact of climate change on berthing areas in ports of the Balearic Islands: adaptation measures

Sierra,

Sánchez-Arcilla,

Gironella

et al. 2023

Front. Mar. Sci.

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Climate change generates impacts on coastal areas due to sea-level rise and potential modifications in wave and storm surge patterns. Since harbours are located in littoral areas, they will experience different impacts associated to such processes. In this paper, the effects of climate change on port berthing areas in terms of operability are quantified. The study is focused on the ports of the Balearic Islands (Western Mediterranean Sea) and analyses the loss of operability due to the reduction of freeboard in berthing structures and the potential variation in agitation within these harbours during the 21st century, considering two different climate scenarios (RCP4.5 and RCP8.5) and two-time horizons (2045 and 2100). In addition, adaptation measures to address such impacts are proposed and their cost estimated. The results indicate that climate change will not generate significant changes in wave agitation due to negligible variations in wave patterns under future scenarios. On the contrary, sea-level rise will cause huge increases of inoperability for berthing structures due to insufficient freeboard: 10.5% under RCP4.5 or 20.5% under RCP8.5 in 2045, increasing to 57.1% (RCP4.5) and even 83.2% (RCP8.5) in 2100.

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Section: Figurementioning

confidence: 99%

Impact of climate change on berthing areas in ports of the Balearic Islands: adaptation measures

Sierra,

Sánchez-Arcilla,

Gironella

et al. 2023

Front. Mar. Sci.

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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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“…The R values were observed and utilized as performance indicators throughout the validation and testing phases. The R value was utilized in the validation phase to assess network generalization, terminate the simulation when generalization ceased to improve, and determine the optimum architecture, while the R value in the testing phase serves as an additional independent measure of network performance during and after the simulation [85,86]. In a simulation using the NN-PSO method, the MSE was minimized which includes the overall MSE in the validation and testing phases.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Jesus

Senoro

Cruz

et al. 2021

Toxics

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Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson's correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.

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Wave estimation within a port using a fully nonlinear Boussinesq wave model and artificial neural networks

Cited by 34 publications

References 44 publications

Impact of climate change on berthing areas in ports of the Balearic Islands: adaptation measures

Impact of climate change on berthing areas in ports of the Balearic Islands: adaptation measures

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

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

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