Towards Proactive Disaster Resilience

Abstract

Modern Disaster Management Systems (DMS) are currently shifting from reactive hazard response to proactive, intelligent resilience architectures. As global hazards escalate in both frequency and severity, classical mitigation lifecycles are increasingly burdened by severe latency in information processing and resource allocation. To address these shortcomings, a comprehensive, Artificial Intelligence-driven framework for disaster management is proposed, which synthesizes state-of-the-art computational models with foundational hazard theories. By integrating Geographic Information Systems (GIS), Internet of Things (IoT) distributed sensor networks, and sophisticated multimodal data fusion techniques, this proposed system accurately processes disparate spatial and environmental data streams in real time. Crucially, the architecture utilizes the Gemini 2.5 multimodal model to orchestrate extreme-scale context management and to execute next-generation agentic capabilities across decentralized emergency response networks. Furthermore, the paper critically examines the socio-technical challenges associated with crisis management AI and proposes explicit methodologies to mitigate spatial blind spots in vulnerability mapping as well as algorithmic bias. The ultimate result is a translational, highly scalable AI framework engineered for equitable, real-world deployment.