A hybrid LLM and machine learning framework for early fire detection in subway tunnels

Fire detection is vital in subway tunnels where confined geometries and ventilation complicate safety monitoring. Existing approaches, including classical machine learning methods, typically detect fire from multivariate correlations but often lack the contextual reasoning required for disambiguation. This limitation becomes critical when HVAC-driven airflow disrupts thermal stratification and dilutes gas concentrations, creating ambiguous patterns that mimic fire signatures. Recent studies suggest that Large Language Models (LLMs) may help address this challenge by translating structured sensor summaries into concise semantic descriptions. To examine this role in fire detection, we propose HyFiD, a hybrid framework that employs an LLM as a semantic feature extractor to augment classical classifiers. By converting momentary multi-sensor readings (temperature, smoke, O[Formula: see text], CO, and CO[Formula: see text]) into textual assessments of environmental states, HyFiD generates semantic vectors that are fused with numerical features for supervised classifier training. Experiments on Fire Dynamics Simulator (FDS)-based subway scenarios, including ventilation-dominated HVAC events and high-energy battery fires, show that the effect of LLM-derived semantic features is strongly dependent on the downstream classifier. Within the evaluated FDS-based scenarios, the GBM-based HyFiD configuration achieved the highest Accuracy, Recall, and F1 among the tested hybrid models, numerical-only deep learning baselines, and direct prompt-only LLM baselines. The results further show that semantic augmentation should be assessed not only by classification accuracy, but also by operational alarm behavior such as detection delay and pre-alarm rate.