Review-Based Conceptual Framework for Generative AI in Urban Disaster Management

Abstract

Natural disasters, including floods and earthquakes and cyclones and wildfires, create ongoing dangers to human life and critical infrastructure and worldwide socioeconomic stability. The rising occurrence and intensity of these events require disaster management systems that can think and respond and adapt to changing needs. Generative Artificial Intelligence (GenAI) has become the dominant framework for disaster response activities. The system can create realistic simulations of disaster situations by processing extensive multimodal data. The research investigates how GenAI functions in disaster management while focusing on disaster prediction and damage assessment and response planning and resilience building. The system enables crisis management through its advanced satellite image analysis and social media text processing and sensor data evaluation and visual record assessment. The collaborative process of creating models enables urban resilience planning through scenario simulation because it shows disaster effects under different conditions. Data privacy issues together with ethical challenges and model reliability problems and misinformation risks remain as major obstacles. The study combines existing research to describe what GenAI currently achieves and what it cannot do in disaster management while presenting future research routes that will help build AI systems which are safe and reliable and beneficial for human users and will boost worldwide disaster resilience.