RISK ASSESSMENT IN THE PROCESS OF IMPLEMENTING AN IMPROVED MODEL FOR MANAGING ENVIRONMENTAL RISKS IN A TESTING LABORATORY
DOI:
https://doi.org/10.31732/2663-2209-2026-81-293-300Keywords:
environmental risks, testing laboratory, integral risk index, risk management, artificial intelligence, environmental managementAbstract
The article addresses the issue of enhancing the effectiveness of environmental risk management in testing laboratories, where the increasing complexity of technological processes and the growing requirements of international accreditation standards necessitate the development of advanced approaches to identifying and mitigating potential threats. The aim of the study is to assess risks in the process of implementing an improved risk management model based on the use of the integral risk index and algorithms for forecasting its dynamic changes. The scientific novelty lies in the application of a comprehensive approach that combines mathematical modeling, risk level classification, and artificial intelligence tools to increase the accuracy and efficiency of managerial decisions.
In the course of the study, the integral risk index was applied as a universal indicator allowing for the aggregation of heterogeneous risk parameters into a unified assessment system. The exponential decline model demonstrated a long-term trend toward reducing the overall level of risk. The proposed classification scale of risks enabled the identification of priority management areas and the optimization of resource allocation. An additional contribution is the integration of machine learning algorithms (Random Forest, XGBoost, LSTM), which enhanced the accuracy of risk scenario forecasting and revealed hidden interdependencies among environmental factors. This confirmed the relevance of applying intelligent technologies to environmental management.
The findings highlight the practical significance of the proposed model in ensuring the reliability and resilience of testing laboratories, as well as its compliance with modern environmental safety requirements and international standards. The results obtained may serve as a foundation for developing risk management systems in industrial and critical infrastructure facilities, thus opening perspectives for further interdisciplinary research in the field of digitalization and intellectualization of environmental management.
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