Cargando…

Using Deep Learning to Forecast Maritime Vessel Flows

Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhou, Xiangyu, Liu, Zhengjiang, Wang, Fengwu, Xie, Yajuan, Zhang, Xuexi
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2020
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146195/ https://www.ncbi.nlm.nih.gov/pubmed/32235812 http://dx.doi.org/10.3390/s20061761

Ejemplares similares

Maritime route and vessel tracklet dataset for vessel-to-route association
por: Iphar, Clément, et al.
Publicado: (2022)

Spatiotemporal dynamic network for regional maritime vessel flow prediction amid COVID-19
por: Zhao, Chuan, et al.
Publicado: (2022)

Probabilistic Maritime Trajectory Prediction in Complex Scenarios Using Deep Learning
por: Sørensen, Kristian Aalling, et al.
Publicado: (2022)

Maritime governance: speed, flow, form process
por: Roe, Michael
Publicado: (2016)

Maritime sector at verge of change: learning and competence needs in Finnish maritime cluster
por: Kilpi, Vesa, et al.
Publicado: (2021)

Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey
por: Cevik, Fatma Carman, et al.
Publicado: (2023)

Dynamic capabilities of maritime infrastructure: conceptual design of merchant vessels with usability in crisis
por: Hirayama, Satoshi, et al.
Publicado: (2023)

Every Vessel Counts: Neural Network Based Maritime Traffic Counting System
por: Petković, Miro, et al.
Publicado: (2023)

Decision-Making for the Autonomous Navigation of Maritime Autonomous Surface Ships Based on Scene Division and Deep Reinforcement Learning
por: Zhang, Xinyu, et al.
Publicado: (2019)

ADST: Forecasting Metro Flow Using Attention-Based Deep Spatial-Temporal Networks with Multi-Task Learning
por: Jia, Hongwei, et al.
Publicado: (2020)

Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach
por: Hoeft, Michal, et al.
Publicado: (2022)

A Deep Learning Framework About Traffic Flow Forecasting for Urban Traffic Emission Monitoring System
por: Yao, Baozhen, et al.
Publicado: (2022)

Maritime Quarantine
por: Wheeler, William A.
Publicado: (1894)

Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture
por: Ilias, Loukas, et al.
Publicado: (2023)

Forecasting future Humphrey Visual Fields using deep learning
por: Wen, Joanne C., et al.
Publicado: (2019)

COVID-19 in Iran: Forecasting Pandemic Using Deep Learning
por: Kafieh, Rahele, et al.
Publicado: (2021)

Multi-step rainfall forecasting using deep learning approach
por: Narejo, Sanam, et al.
Publicado: (2021)

Forecasting Directional Movement of Stock Prices using Deep Learning
por: Chandola, Deeksha, et al.
Publicado: (2022)

SALA-LSTM: a novel high-precision maritime radar target detection method based on deep learning
por: Wang, Jingang, et al.
Publicado: (2023)

Deep learning for twelve hour precipitation forecasts
por: Espeholt, Lasse, et al.
Publicado: (2022)

A Deep Learning-Based Fault Detection Model for Optimization of Shipping Operations and Enhancement of Maritime Safety
por: Theodoropoulos, Panayiotis, et al.
Publicado: (2021)

Learning with Weak Annotations for Robust Maritime Obstacle Detection
por: Žust, Lojze, et al.
Publicado: (2022)

Climate change and maritime security narrative: the case of the international maritime organisation
por: Germond, Basil, et al.
Publicado: (2018)

City-Wide Traffic Flow Forecasting Using a Deep Convolutional Neural Network
por: Sun, Shangyu, et al.
Publicado: (2020)

Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning
por: Chattopadhyay, Ashesh, et al.
Publicado: (2020)

Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems
por: Bhatt, Pratyush, et al.
Publicado: (2023)

Forecasting COVID-19 new cases using deep learning methods
por: Xu, Lu, et al.
Publicado: (2022)

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning
por: Ahmed, Sabbir, et al.
Publicado: (2022)

Forecasting commodity prices: empirical evidence using deep learning tools
por: Ben Ameur, Hachmi, et al.
Publicado: (2023)

The Great Maritime Discoveries
por: Taylor, Rodney H.
Publicado: (1993)

The Maritime Medical News
Publicado: (1888)

Deep learning forecast of rainfall-induced shallow landslides
por: Mondini, Alessandro C., et al.
Publicado: (2023)

Stock Market Forecasting Based on Spatiotemporal Deep Learning
por: Li, Yung-Chen, et al.
Publicado: (2023)

Deep Learning Networks Accurately Detect ST-Segment Elevation Myocardial Infarction and Culprit Vessel
por: Wu, Lin, et al.
Publicado: (2022)

A Multi-AUV Maritime Target Search Method for Moving and Invisible Objects Based on Multi-Agent Deep Reinforcement Learning
por: Wang, Guangcheng, et al.
Publicado: (2022)

Hepatic vessels segmentation using deep learning and preprocessing enhancement
por: Alirr, Omar Ibrahim, et al.
Publicado: (2023)

Assessment of global shipping risk caused by maritime piracy
por: He, Zhaoyang, et al.
Publicado: (2023)

Empirical mode decomposition using deep learning model for financial market forecasting
por: Jin, Zebin, et al.
Publicado: (2022)

Future forecasting prediction of Covid-19 using hybrid deep learning algorithm
por: Yenurkar, Ganesh, et al.
Publicado: (2022)

Characteristic mango price forecasting using combined deep-learning optimization model
por: Ma, Xiaoya, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_pokrma3mk5i5ir4ucltsoqbmj6