A new machine learning framework for occupational accidents forecasting with safety inspections integration

TL;DR

This paper introduces a versatile machine learning framework that forecasts occupational accidents by integrating safety inspections into binary time series models, providing actionable short-term risk predictions for improved safety management.

Contribution

It presents a novel, model-agnostic framework that leverages safety inspections for short-term accident forecasting, with a focus on operational reliability and decision-making utility.

Findings

The framework reliably predicts high-risk periods for accidents.

Different machine learning models perform robustly within the framework.

Aggregated weekly and daily risk scores aid proactive safety interventions.

Abstract

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.