Post-earthquake functionality assessment and emergency base station deployment optimization for communication systems

Communication systems are essential for post-earthquake emergency response, yet their vulnerability to seismic damage significantly impacts rescue operations. This paper presents a GIS-integrated framework for assessing post-earthquake functionality of the communication system and optimizing emergency base station deployment to restore network coverage. The framework first predicts seismic damage to building portfolios using a machine learning model. This building damage is then integrated into dependency-based models to assess the functionality of base stations. At the system level, connectivity reliability is quantified through a hybrid Monte Carlo–Warshall scheme that propagates stochastic component failures to network reachability. A contribution is a damage-adaptive coverage assessment framework that incorporates GIS visual analysis and building damage states to calculate coverage realistically. Two indexes for the functionality analysis of communication systems have been defined considering coverage area reduction and signal intensity loss. To address identified communication blind areas, a Scenario-Adaptive Genetic Algorithm for emergency base stations is developed to maximize restoration efficiency. Comparative analysis demonstrates that this optimization method significantly outperforms traditional strategies. Finally, the efficacy of the framework is validated through a regional case study. This method provides support for assessing the damage to communication systems and facilitates rapid communication restoration during the emergency response.