Advanced AI Technique and IoT Integration for Predicting and Preventing Flammable Gas and Toxic Vapor Leaks

This study’s ambition is to develop and integrate an advanced AI approach with IoT networks to quantitatively predict and prevent flammable gas and toxic vapor emissions. The main goal of such integration is to enhance safety and minimize the adverse effects on the surrounding environment. Ensuring high-quality, consistent data from various IoT sensors and integrating diverse data sources in real-time is challenging and crucial for AI model performance. Deploying AI models in real-world environments demands robust real-time processing, low latency, and high reliability, especially in dynamic industrial settings with potential connectivity and resource constraints. Deep Learning and XGBoost techniques will be utilized to address issues with data quality, integration, and real-time processing in the prediction and prevention of flammable gas and toxic vapor leaks, aiming to improve accuracy and operational efficiency. Leveraging Advanced AI techniques allows for proactive identification and timely alerting of potential gas leaks, thereby strengthening preemptive safety protocols. This technology supports ongoing monitoring and adaptive response functionalities, effectively reducing downtime and operational disturbances within industrial settings. The overall performance of the XGBoost classifier was 89%, precision 83%, Recall 100%, F1 score 91%. Time taken was 06 seconds, the neural network model achieved 94% accuracy, 91% precision, 100% recall and F1 score was 95%. As for the training time, this model took 5. 10 seconds.