Risk assessment and machine learning models

Abstract

Safety management system for operational domains with extensive scope is a challenging issue. In the current safety management system literature, there are not efficient methods to implement subjective inferences of organization experts to analyze data and predict the performance of safety and quality management systems by using all available data and identify weakness points to improve organizational safety and quality process. Machine learning is a subset of artificial intelligence that involves the algorithms and model development to allow safety and quality management systems that improve their performance over time automatically. The benefits of machine learning include efficiency improvement, reduced costs, improved decision-making, and increased innovation to study the future of risk management. The existence of massive data in operational sectors is a critical challenge for machine learning implementation in safety risk management systems. A safety data pool from all occurrences and hazards should be designed to improve and know about the future of safety management system, and then design the system to assess, analyze, verify, and predict the safety assessment result to decide proper management system decisions. In this study, the machine learning method implementations are proposed for risk management in operational domains.