Data-driven approaches for accident analysis in sociochemical systems

Accident analysis is crucial for gaining a deep understanding of system malfunctions and preventing catastrophic events, along with potential human, financial, and environmental losses. While conventional analysis approaches have advanced our understanding of system failures, they often rely on human judgment and are susceptible to bias. To enhance incident analysis, machine learning and data-driven approaches play an essential role by providing objective insights, revealing hidden patterns, and enabling proactive risk mitigation. These advanced techniques empower organizations to learn from incidents more effectively, thereby enhancing safety, resilience, and overall reliability performance. This chapter reviews the latest scientific research to shed light on the applications, significance, and contributions of machine learning and data-driven techniques in accident modeling and its associated concerns. It explores these aspects in three primary domains: (a) traffic accidents, (b) occupational accidents, and (c) process accidents. Furthermore, this chapter offers valuable insights into the primary challenges, gaps, and demands in accident analysis, considering both academic and industrial perspectives with a focus on machine learning and data-driven viewpoints.