APPLICATION OF ARTIFICIAL INTELLIGENCE-BASED RISK ASSESSMENT METHODS IN HSE MANAGEMENT SYSTEMS

Ziya HUSEYNZADE, Agashirin Guliyev

Improving the level of safety in the industrial environment and minimizing occupational risks are considered key priorities of HSE systems. Under conditions of modern technological development, the increasing complexity of risks arising in industrial enterprises limits the effectiveness of traditional assessment methods and necessitates the application of innovative approaches. This article examines the use of artificial intelligence technologies in the identification and management of risks within HSE systems. The possibilities of applying machine learning algorithms, neural network models, and intelligent data processing methods for detecting hazardous situations and predicting potential accidents are analyzed. It is demonstrated that the analysis of data obtained from sensor systems, surveillance cameras, and production indicators makes it possible to identify potentially dangerous situations in advance. It is noted that artificial intelligence-based systems contribute to reducing errors associated with the human factor, improving the efficiency of safety-related decision-making, and enhancing accident risk assessment processes. Within the framework of the study, traditional risk analysis methods were compared with intelligent models, emphasizing that artificial intelligence approaches provide greater accuracy and flexibility. In addition, the prospects for the broad application of these technologies in automating safety control at industrial facilities, detecting unsafe behavior, and ensuring early identification of emergency situations are discussed.