Citation: Zhang, W.; Zhang, Y.; Qiao,
W. Risk Scenario Evaluation for
Intelligent Ships by Mapping
Hierarchical Holographic Modeling
into Risk Filtering, Ranking and
Management. Sustainability 2022, 14,
2103. https://doi.org/10.3390/
su14042103
Academic Editors: João Carlos de
Oliveira Matias and Paolo Renna
Received: 25 January 2022
Accepted: 9 February 2022
Published: 12 February 2022
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Article
Risk Scenario Evaluation for Intelligent Ships by Mapping
Hierarchical Holographic Modeling into Risk Filtering,
Ranking and Management
Wenjun Zhang
1
, Yingjun Zhang
1,
* and Weiliang Qiao
2
1
Navigation College, Dalian Maritime University, Dalian 116026, China; zhangwenjun@dlmu.edu.cn
2
Marine Engineering College, Dalian Maritime University, Dalian 116026, China; xiaoqiao_fang@dlmu.edu.cn
* Correspondence: zhangyj@dlmu.edu.cn
Abstract:
To identify and screen the risk scenarios for the navigation risk of intelligent ships, the
analysis and evaluation of navigational risks were performed in this study. Risk scenarios were de-
veloped and evaluated by mapping the hierarchical holographic modeling (HHM) into risk filtering,
ranking and management (RFRM). In detail, considering the insignificant influences of some factors
on navigational activities, risk factors were filtered and ranked using the RFRM model. Seven final
factors were successfully determined, including traffic flow, navigation environment understand-
ing, ship–shore interaction capabilities, target recognition capabilities, communication equipment
reliabilities, professional skills, and situation judgments. The results indicated that cargo security
can be guaranteed by following navigational risk identification and screening steps, and thus our
findings provide theoretical guidance for the dynamic management of maritime organizations and
ship companies. In addition, the proposed methodology is desirable for making predictions on
maritime traffic risks.
Keywords: intelligent ships; risk identification; risk scenarios; HHM-RFRM
1. Introduction
The rapid development of artificial intelligence in the maritime industry has promoted
the probability of operating ocean-going intelligent ships. According to documents issued
by the Maritime Safety Committee (MSC) affiliated with the International Maritime Orga-
nization (IMO) [
1
–
3
], it can be reasonably speculated that intelligent ships would play an
important role in the sustainable development of the maritime industry. Obviously, the
capacity for the autonomous sailing of intelligent ships has the advantages of high effi-
ciency, energy saving and security. However, the safety issues associated with intelligent
ships challenge their application, which has to be addressed for the sustainable develop-
ment of artificial intelligence in the maritime industry. As early as 2006, “e-Navigation”
was presented by the IMO, indicated as the birth of intelligent ships [
4
]. Later, in 2007,
the new generation of ships, named unmanned surface vessels (USVs), made their first
appearance on the 98th MSC [
5
]. However, real-time information interactions between
shore-based stations and USVs present a serious challenge. When the sailing distance is
beyond the influence of a navigational communication system, the power of the USVs
would inevitably be lost. Subsequently, in 2018, the USV was further redefined as the
Maritime Autonomous Surface Ship (MASS) on the 99th MSC, able to sail autonomously
and receive/send information from/to stations [6]. According to the automation levels of
an operating system, the controlled performance of ships is divided into four levels. On the
first level, the ship operating system is directly controlled by crews, and only a small part
of the systems can run automatically. On the second level, the main system of the ship
can be operated automatically or controlled remotely by crews, while the failure diagnosis
depends on manual operations; as a result, some extra crews are necessary to guarantee
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