Port Resilience Assessment for Misdeclaration Induced Disasters Using a Hybrid LLM-GNN Framework

Abstract

Ports face critical security threats from hazardous cargo misdeclaration, which poses unique challenges due to its high concealment and catastrophic potential, as exemplified by the Beirut Port explosion. Traditional resilience assessment approaches relying on hazard state transition probabilities require abundant historical data or extensive domain expertise for probability elicitation, and static indicator-based assessment frameworks fail to capture the spatiotemporal evolution characteristics of disasters. To address these challenges, this study proposes a hybrid framework that leverages the Large Language Model (LLM)’s generalizable world knowledge for data augmentation while developing a Spatiotemporal Graph Neural Network (STGNN) to predict dynamic disaster propagation. Specifically, a multimodal LLM is employed to extract structured port state descriptions from temporally aligned disaster data and infer the states at undocumented time steps. With more disaster scenarios adapted from the real cases using the LLM, a STGNN is trained to learn the disaster evolution dynamics and make efficient real-time inference for resilience assessment and intervention strategy evaluation. Validation on Tianjin and Beirut Port incidents demonstrates that the framework accurately predicts disaster propagation pathways and identifies critical intervention priorities. It also reveals that topology-based intervention strategies substantially accelerate recovery, while adverse environmental conditions significantly amplify cumulative functional loss. This study represents an advancement toward AI-driven resilience modeling, offering port operators and regulators an adaptable, scalable decision support tool for intelligent safety governance.