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预测与人类有关的海上事故类型,并使用选择性集体学习和SHAP方法进行解释
He Lan1, Shutian Wang1, Wenfeng Zhang1
1School of Economics and Management, Dalian Ocean University, Dalian, 116023, China.
Heliyon
|May 2, 2024
概括
这项研究引入了一种新的两阶段模型,用于预测海上事故类型,达到87.50%的准确性. 可解释模型确定了关键的风险因素,为海事利益相关者提供了积极的安全干预措施.
科学领域:
- 海上安全的航行.
- 事故预测 事故预测
- 可解释的人工智能
背景情况:
- 海上事故造成重大损失和生命损失.
- 准确的风险评估对海事利益相关者至关重要.
- 现有的预测模型缺乏可解释性和稳定性.
研究的目的:
- 为海上事故类型开发一种混合,可解释的预测模型.
- 为了提高预测准确性和模型稳定性.
- 确定与海员不安全行为相关的风险因素.
主要方法:
- 一种基于选择性集体学习的新两阶段模型选择方法.
- 整合了SHAP (沙普利添加式解释) 方法的解释性.
- 为海上事故类型开发混合预测模型.
主要成果:
- 开发的模型实现了87.50%的预测准确率和84.98%的F1得分.
- 确定了海员不安全行为,风险因素和预测结果之间的有效映射关联.
- 该模型为海上事故预测提供了可解释的见解.
结论:
- 这种混合型的可解释模型显著改善了海上事故类型的预测.
- 这些发现使利益相关者能够做出明智的决定,采取主动干预.
- 这项研究通过先进的风险评估,有助于加强海上安全.
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