A novel hybrid deep-learning framework for medium-term container throughput forecasting: an application to China’s Guangzhou, Qingdao and Shanghai hub ports-Reference-Cited by-同舟云学术

A novel hybrid deep-learning framework for medium-term container throughput forecasting: an application to China’s Guangzhou, Qingdao and Shanghai hub ports

Published:2024-02-16 Issue:1 Volume:26 Page:44-73
ISSN:1479-2931
Container-title:Maritime Economics & Logistics
language:en
Short-container-title:Marit Econ Logist

Author:

Zhang Di,Li Xinyuan,Wan Chengpeng,Man Jie

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1057/s41278-024-00284-2.pdf

Reference56 articles.

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3. Cuong, T.N., L.N.B. Long, H.-S. Kim, and S.-S. You. 2022. Data analytics and throughput forecasting in port management systems against disruptions: A case study of Busan Port. Maritime Economics & Logistics 25 (1): 61–89. https://doi.org/10.1057/s41278-022-00247-5.

4. Dong, L., Z. Chen, R. Hua, S. Hu, C. Fan, and X. Xiao. 2023. Research on diagnosis method of centrifugal pump rotor faults based on IPSO-VMD and RVM. Nuclear Engineering and Technology 55 (3): 827–838. https://doi.org/10.1016/j.net.2022.10.045.

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