IoT and AI-Based Approaches for Monitoring and Preventing Hazards from Inflammable Materials

Abstract

Inflammable materials pose significant risks in industrial, residential, and transportation environments, often leading to catastrophic fire accidents and economic losses. Traditional hazard detection and safety management systems rely on manual monitoring or delayed response mechanisms, which limit their effectiveness. This research proposes an integrated approach using the Internet of Things (IoT) and Artificial Intelligence (AI) for real-time monitoring, prediction, and prevention of hazards related to inflammable materials. IoT-based sensor networks were deployed to continuously track temperature, gas concentration, and environmental parameters, while AI algorithms analyzed the data to identify early warning signs of ignition risks. A hybrid machine learning model combining Support Vector Machines and Deep Neural Networks achieved high accuracy in hazard prediction, significantly reducing false alarms. Experimental simulations demonstrated that the proposed system improves response time and enhances overall safety. The findings suggest that the integration of IoT and AI can serve as a scalable, cost-effective solution for proactive fire prevention and risk management in industries handling inflammable substances.