Navigating Fragmented Research: A Model–Data–Scenario Adaptation (MDSA) Framework for Sustainable Accident Prediction and Risk Governance in High-Risk Industries

Proactive accident prediction is a fundamental prerequisite for the environmental and social sustainability of high-risk sectors. Accident prediction research has expanded rapidly across transportation, construction, fire safety, chemical/process industries, and mining, yet many models that perform well in offline benchmarks fail in field deployment because algorithm capability, data regime, and operational constraints are misaligned. This review synthesizes cross-industry evidence on how accident prediction is practiced under distinct data conditions, including spatiotemporal, multimodal, and data-scarce settings, and compares mainstream methods from statistical baselines to machine learning and deep learning in terms of deployability rather than accuracy alone. Building on this synthesis, we propose the Model–Data–Scenario Adaptation (MDSA) framework, a systems-level protocol that operationalizes deployment-aware model selection through a multi-dimensional scoring rubric and an iterative validation loop. MDSA balances predictive performance with interpretability, robustness, data dependency, and implementation cost. A chemical industry case study demonstrates how accuracy-centric selection can fail operationally and how MDSA yields a more viable choice under real constraints. The framework ultimately facilitates long-term sustainable risk governance by balancing predictive performance with operational constraints, thereby contributing to the United Nations Sustainable Development Goals (SDGs 3, 8, 9, and 11).