Affiliations: [a] Marine Intelligent Ship Engineering Research Center of Fujian Province Colleges and Universities, Minjiang University, Fuzhou, China
| [b] National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China
| [c] Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, Chongqing, China
| [d]
Changjiang Waterway Planning Design and Research Institute, Wuhan, China
| [e] Department of Computer Engineering, University of Alcalá, Alcalá de Henares (Madrid), Spain
| [f] School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW, Australia
Abstract: The Three Gorges ship lock, as one of the world leading navigational architectures, plays a significant role in the inland water transport of China. The rapid development of marine transportation increases great pressure on the maritime monitoring and administration in the ship locks. Hence, it is essential to explore the navigational risk factors. This study proposes a new dynamics model to investigate the evolution mechanism of navigational risk in Three Gorges ship lock. Intelligent expert system was used to analyze the risk factors. Experimental validation results demonstrate that the weather may impact the ship navigational safety with a high risk probability. Meanwhile, the navigational risk in flood season is relatively higher than that in dry season due to the increase of flow velocity and rain fall. The present study may provide useful insights to the evolution of inland navigational risk as well as maritime administration in the Three Gorges ship lock.
Keywords: Ship lock, intelligent expert system, data mining, field data, navigational risk
