Predicting Toxic Gas Levels in Structure Fires Using Machine Learning

This study presents a machine learning framework for real-time hazard assessment in fire environments using gas sensor data. A two-stage predictive model was developed with simulated data from PyroSim, a fire dynamics tool. The regression model predicts gas concentrations throughout a burning structure, while the classification model determines whether the levels are safe or pose an immediate danger to life and health (IDLH). To enhance performance, lag features captured short-term temporal dynamics, and the Synthetic Minority Over-sampling Technique (SMOTE) addressed class imbalance, improving the detection of rare but critical hazardous conditions. This approach significantly increased the model's accuracy in identifying immediate danger events. Designed for real-time deployment, the system processes sensor input to provide instant hazard classification. Preliminary results indicate that the models accurately predict and classify hazardous conditions with high confidence, enabling real-time use in autonomous systems. Future efforts will focus on sensor calibration, system integration, and field validation to advance automated risk assessment and improve responder safety in fire emergencies.