A new machine learning framework for occupational accidents forecasting with safety inspections integration
融合安全检查的职业事故预测机器学习框架AI 解读 ↓
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融合安全检查的职业事故预测机器学习框架
- 研究问题
- 如何将日常安全检查记录转化为职业事故的短期预测，并把每日事故风险汇总为可用于安排检查与干预的周度评估？
- 方法与思路
- 作者把事故发生建模为二元时间序列，比较逻辑回归、树模型与神经网络，采用滑动窗口验证和期间汇总指标评价预测。
- 材料陈述的贡献
- 摘要称该框架在所测试算法中能够识别未来高风险时段，并将检查数据转为日度和周度风险分数，支持安全资源安排。
- 阅读关注点
- 应核对检查信息在预测时是否可得、窗口划分与汇总规则，以及稀有事故情形下的校准表现和跨场景泛化能力。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明样本、具体指标和数值，也未验证干预实际降低事故的效果。
原文摘要
We propose a model-agnostic framework for short-term occupational accident forecasting that leverages safety inspections and models accident occurrences as binary time series. The approach generates daily predictions, which are then aggregated into weekly safety assessments for better decision making. To ensure the reliability and operational applicability of the forecasts, we apply a sliding-window cross-validation procedure specifically designed for time series data, combined with an evaluation based on aggregated period-level metrics. Several machine learning algorithms, including logistic regression, tree-based models, and neural networks, are trained and systematically compared within this framework. Across all tested algorithms, the proposed framework reliably identifies upcoming high-risk periods and delivers robust period-level performance, demonstrating that converting safety inspections into binary time series yields actionable, short-term risk signals. The proposed methodology converts routine safety inspection data into clear weekly and daily risk scores, detecting the periods when accidents are most likely to occur. Decision-makers can integrate these scores into their planning tools to classify inspection priorities, schedule targeted interventions, and funnel resources to the sites or shifts classified as highest risk, stepping in before incidents occur and getting the greatest return on safety investments.