Integrating smart fire forecast with LLM-powered emergency response

Abstract

Existing data-driven fire forecast systems often exhibit limitations in real-world emergency response scenarios, particularly with respect to efficient data reuse and vulnerability of sensor networks. This study proposes a smart agent that integrates an artificial intelligence (AI)-driven fire situational awareness engine with a large language model (LLM) to realize the diverse demands of emergency response in complex fire scenarios. First, a fire-resilient deep learning model based on ConvLSTM is developed to reconstruct building temperature fields using limited inputs from a partially failed temperature sensor network. The proposed architecture constructs spatiotemporal correlations between missing and survived sensor data, enabling the transformation of discrete temperature measurements into a continuous two-dimensional (2D) temperature contour. Subsequently, a smart agent powered by a domain-specific LLM is designed to enhance human–AI interaction during fire emergency response. A self-driven framework capable of automatically executing LLM-generated programs is established to deliver real-time, user-specific information to multiple stakeholders. Experimental results demonstrate that, compared with generic LLM-based responses, the proposed agent augmented with fire situational awareness can generate customized operational recommendations through dynamic interactions with the ConvLSTM-based fire model. This hybrid agent improves situational awareness and safety during fire emergencies, improves the resilience of fire services systems, and advances the practical implementation of AI-driven smart firefighting.