通过使用自动机器学习来预测自然灾害造成的海港干扰
Rafi Ullah Khan1, Jingbo Yin1, Mujtaba Asad2
1State Key Laboratory of Ocean Engineering, Department of Transportation Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Marine pollution bulletin
|July 25, 2025
概括
自动机器学习 (AutoML) 模型可以预测自然灾害引起的海港干扰. 神经网络实现了最高的准确性,突出了诸如危险严重程度和恢复效率等关键因素,以提高海港弹性.
科学领域:
- 海上物流和灾害管理.
- 人工智能在供应链风险评估中的应用.
背景情况:
- 海港对全球贸易至关重要,但自然灾害威胁其运营和经济稳定.
- 预测自然灾害造成的海港中断对于确保供应链连续性至关重要.
研究的目的:
- 评估自动机器学习 (AutoML) 模型在预测自然灾害引起的海港干扰方面的有效性.
- 识别最准确的AutoML模型和影响性特征,用于中断预测.
主要方法:
- 利用了2010年至2019年的海港中断数据集.
- 在七个类别中评估了24个AutoML模型,包括神经网络和支持向量机器.
- 执行功能排名以确定影响中断预测的关键因素.
主要成果:
- 神经网络表现出最高的预测准确性 (94.95%),广泛的神经网络达到96.44%.
- 支持向量机的准确性最低 (76.32%).
- 危险的"严重程度"和"恢复"被确定为最有影响力的因素.
结论:
- 自动ML模型,特别是神经网络,显示出预测海港干扰的巨大潜力.
- 调查结果为提高海港抗灾能力和应对自然灾害的准备提供了洞察力.
- 未来的研究应该专注于结合更细致的数据和集合模型,以提高预测能力.
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