A novel high-precision and self-adaptive prediction method for ship energy consumption based on the multi-model fusion approach

Energy, Год журнала: 2024, Номер unknown, С. 133265 - 133265

Опубликована: Сен. 1, 2024

Язык: Английский

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An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

JOIV International Journal on Informatics Visualization, Год журнала: 2024, Номер 8(1), С. 158 - 158

Опубликована: Март 31, 2024

This review article looks at the developing field of artificial intelligence and machine learning in maritime marine environment management. The industry is increasingly interested applying advanced AI ML technologies to solve sustainability, efficiency, regulatory compliance issues. paper examines applications using a deep literature case study analysis. Modeling ship fuel consumption, which impacts operating expenses, top responsibility. demonstrates that approaches such as Random Forest Tweedie models can estimate use. Statistical analysis model beats regarding accuracy consistency. For training testing datasets, has high R2 values 0.9997 0.9926, indicating solid match. Low Root Mean Square Error (RMSE) average absolute relative deviation (AARD) suggest accurately reflects use variability. While still performing well, lower higher RMSE AARD values, suggesting reduced precision consumption prediction. These findings provide light on potential Advanced analytics enables decision-makers analyze patterns better, increase operational decrease environmental impact, thus improving sustainability.

Язык: Английский

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Incorporation of energy-consumption optimization into multi-objective and robust port multi-equipment integrated scheduling

Transportation Research Part C Emerging Technologies, Год журнала: 2024, Номер 166, С. 104755 - 104755

Опубликована: Июль 15, 2024

Язык: Английский

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Benchmarking feed-forward randomized neural networks for vessel trajectory prediction

Computers & Electrical Engineering, Год журнала: 2024, Номер 119, С. 109499 - 109499

Опубликована: Июль 31, 2024

Язык: Английский

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Application of switching-input LSTM network for vessel trajectory prediction

Applied Intelligence, Год журнала: 2025, Номер 55(4)

Опубликована: Янв. 9, 2025

Язык: Английский

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HDFormer: A transformer-based model for fishing vessel trajectory prediction via multi-source data fusion

Ocean Engineering, Год журнала: 2025, Номер 320, С. 120309 - 120309

Опубликована: Янв. 13, 2025

Язык: Английский

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Ship fuel consumption prediction based on transfer learning: Models and applications

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 141, С. 109769 - 109769

Опубликована: Дек. 2, 2024

Язык: Английский

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Vessel Trajectory Prediction for Enhanced Maritime Navigation Safety: A Novel Hybrid Methodology

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(8), С. 1351 - 1351

Опубликована: Авг. 8, 2024

The accurate prediction of vessel trajectory is crucial importance in order to improve navigational efficiency, optimize routes, enhance the effectiveness search and rescue operations at sea, ensure maritime safety. However, spatial interaction among vessels can have a certain impact on accuracy models. To overcome such problem predicting trajectory, this research proposes novel hybrid methodology incorporating graph attention network (GAT) long short-term memory (LSTM). proposed GAT-LSTM model comprehensively consider spatio-temporal features process, which expected significantly robustness prediction. Automatic Identification System (AIS) data from surrounding waters Xiamen Port collected utilized as empirical case for validation. experimental results demonstrate that outperforms best baseline terms reduction average displacement error final error, are 44.52% 56.20%, respectively. These improvements will translate into more trajectories, helping minimize route deviations collision avoidance systems, so effectively provide support warning about potential collisions reducing risk accidents.

Язык: Английский

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Geohash coding-powered deep learning network for vessel trajectory prediction using clustered AIS data in maritime Internet of Things industries

Computers & Electrical Engineering, Год журнала: 2024, Номер 120, С. 109611 - 109611

Опубликована: Сен. 19, 2024

Язык: Английский

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A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping

Ocean Engineering, Год журнала: 2023, Номер 286, С. 115687 - 115687

Опубликована: Авг. 31, 2023

Accurate vessel traffic flow (VTF) prediction can enhance navigation safety and economic efficiency. To address the challenge of inherently complex dynamic growth VTF time series, a new hierarchical methodology for is proposed. Firstly, original data reconfigured as three-dimensional tensor by modified Bayesian Gaussian CANDECOMP/PARAFAC (BGCP) decomposition model. Secondly, matrix (hour ✕ day) each week decomposed into high- low-frequency matrices using Bidimensional Empirical Mode Decomposition (BEMD) model to non-stationary signals affecting results. Thirdly, self-similarities between within high-frequency are utilised rearrange different one-dimensional series solve weak mathematical regularity in matrix. Then, Dynamic Time Warping (DTW) employed identify grouped segments with high similarities generate more suitable tensors. The experimental results verify that proposed outperforms state-of-the-art methods real Automatic Identification System (AIS) datasets collected from two areas. potentially optimise relation operations manage traffic, benefiting stakeholders such port authorities, ship operators, freight forwarders.

Язык: Английский

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