Recent trends of machine learning techniques for risk assessment in hazardous environments

Abstract Risk assessment is a critical step in the regulatory decision-making process, carried out within the framework of political and legislative requirements, in addition to the need to make decisions on time according to the available resources. Some critical and hazardous facilities such as nuclear power plants, offshore oil and gas, and hazardous materials storage sites, are very useful to society but are inherently risky. For these facilities, failure has an increased criticality, causing adverse effects on the ecological system and human health. Therefore, the risk assessment process is time-sensitive for such industries. Due to the recent technological development in the industry, the significance of risk management has increased, and the identification, assessment, reporting, and management of risks have received continuous attention. Machine learning is becoming more and more powerful for use in industry applications; many solutions have already been put into practice, and many more are being investigated. Most articles do not review the hazard industries. This review aims at identifying and analyzing the literature on risk assessments for the study of risks, types of consequences, and disaster mitigation, with a focus on literature that uses machine learning approaches, particularly in hazard environments. Retrieved articles are analyzed and reviewed in terms of different risk assessment aspects. Findings and gaps in each article are reported. The results of the analysis prove the power of machine learning approaches in assessing the risk and highlight their use in hazardous environments. Findings also showed that it is an ongoing research topic that needs more studies to achieve the highest benefits. Besides, this review can provide researchers with the future directions in this field.