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ORIGINAL RESEARCH article

Front. Mar. Sci.
Sec. Marine Affairs and Policy
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1522071

SOM neural network-based port function analysis: A case study in 21st-century Maritime Silk Road

Provisionally accepted
Fahao Xie Fahao Xie 1Le Zhang Le Zhang 1*Shanshui Zheng Shanshui Zheng 1Aijun Xu Aijun Xu 1Zhitao Li Zhitao Li 1Jiaxin Dai Jiaxin Dai 1Lang Xu Lang Xu 2
  • 1 School of Transport and Logistics, Guangzhou Railway Polytechnic, guangzhou, China
  • 2 Shanghai Maritime University, pudong, Shanghai, China

The final, formatted version of the article will be published soon.

    The 21st-century Maritime Silk Road initiative by the Chinese government has garnered growing global attention. As pivotal facilitators of international trade, the maritime routes and ports along this route are attracting the interest of various stakeholders. There is a pressing need for extensive research to augment the existing theoretical frameworks. This paper introduces a Self-Organizing Map (SOM) neural network-based methodology for port function clustering, applied to 24 major ports spanning from the region in 2023. The clustering outcomes are cross-validated against port rankings derived from Principal Component Analysis. The study reveals several key insights: (1) Singapore Port, Hong Kong Port, Shenzhen Port, and Guangzhou Port emerge as the principal shipping hubs within the region; (2) The relationship between China and Singapore is identified as a linchpin for the sustainable development of the 21st-century Maritime Silk Road; (3) Guangdong Province is highlighted as a central economic and logistical node. Finally, the recommendations for the accelerated development of the Hainan Free Trade Port and Fujian Coastal Port is concluded.

    Keywords: 21st-Century Maritime Silk Road, SOM neural network, Port function analysis, Principal Component Analysis, South China Sea to the ASEAN

    Received: 03 Nov 2024; Accepted: 19 Dec 2024.

    Copyright: © 2024 Xie, Zhang, Zheng, Xu, Li, Dai and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Le Zhang, School of Transport and Logistics, Guangzhou Railway Polytechnic, guangzhou, China

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