机器学习在海上运输风险评估中的应用:现状和未来方向

IF 8 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yuqing Lin , Xue Li , Kum Fai Yuen
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引用次数: 0

摘要

由于海上运输系统本质上是复杂的,容易受到潜在危险的影响,因此通过适用的方法进行风险评估至关重要。最近,机器学习(ML)算法因其有效分析风险的能力而引起了极大的关注。然而,缺乏对机器学习在海上运输风险评估(MTRA)中的应用的系统总结。因此,本综述旨在利用系统综述和荟萃分析方法概括机器学习应用的现状、问题、考虑和未来方向。特别从优势、劣势以及相应的应用三个方面对现状进行了总结。此外,还认识到数据集处理和方法利用方面的问题,以及敏感性分析和评价方法方面的考虑。关于未来的方向,确定了在数据和方法改进方面有希望的机会。综上所述,本文以一个框架来展示现有的研究现状,并为未来的研究提供模型选择和方法改进的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning applications for risk assessment in maritime transport: Current status and future directions
As the maritime transportation system is inherently complex and vulnerable to potential hazards, it is critical to conduct risk assessments via applicable methodologies. Recently, machine learning (ML) algorithms have attracted tremendous attention due to their ability to analyze risks effectively. Nevertheless, there is a lack of a systematic summarization of ML applications in maritime transport risk assessment (MTRA). Hence, this review aims to encapsulate the current status, issues, considerations, and future directions of ML applications using the systematic reviews and meta-analyses method. In particular, the status is summarized from the following three dimensions: advantages, disadvantages, and corresponding applications. Moreover, several issues are recognized, including dataset processing and methods utilization, and considerations from the perspective of sensitivity analysis and evaluation methods. Regarding future directions, promising opportunities in terms of data and method improvements are identified. Overall, this review contributes to MTRA by presenting the existing research status with a framework and providing suggestions on model selection and method improvement for future research.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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