ENVIRONMENTAL SAFETY AUDIT IN CONDITIONS OF EMERGENCY SITUATIONS BASED ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS TECHNOLOGIES
DOI:
https://doi.org/10.32782/pcsd-2025-4-10Keywords:
environmental safety audit, artificial intelligence, Internet of Things, emergency scenarios, industrial monitoring, intelligent decision support, integrated system architectureAbstract
Ensuring environmental safety under increasing anthropogenic and natural threats requires highly reliable digital systems capable of continuous monitoring, rapid diagnostics, and timely response to hazardous deviations. The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies is becoming a key direction for strengthening the analytical capacity and operational efficiency of environmental safety audit, particularly in emergency scenarios where data volumes, uncertainty, and dynamics significantly intensify. This study provides a systematic examination of contemporary AI and IoT solutions, focusing on their functional properties, constraints, and applicability to enterpriselevel environmental assessment. Special attention is given to the specific operational requirements and challenges associated with emergency conditions, where the reliability, adaptability, and automation of decision-making mechanisms are critical. Based on the conducted analysis, a generalized architecture of an integrated environmental safety audit system is proposed. The architecture incorporates a distributed IoT sensor layer for high-resolution data acquisition, intelligent AI-based modules for state evaluation and anomaly detection, and adaptive operational modes ensuring stable system functionality during transitions from normal to emergency states. The proposed framework is characterized by structural flexibility, scalability, and applicability across diverse industrial domains, enabling improved situational awareness and more accurate environmental risk assessment. The results obtained substantiate the effectiveness of combining AI and IoT for strengthening technological resilience and enhancing the precision of environmental safety audit. The proposed architecture forms a methodological foundation for further research aimed at expanding system autonomy, improving predictive capabilities, and increasing robustness under complex and rapidly evolving risk conditions.
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