AI-Integrated Disaster Risk Reduction System for Real-Time Response and Prevention in Vulnerable Regions

In vulnerable regions, disasters present a significant threat, and such areas need advanced risk reduction systems that can provide real-time response and prevention. The ability to predict, respond efficiently, and allocate resources to the earthquake disaster has been proposed by the Adaptive AI-Driven Disaster Resilience Framework (AIDR-F), an innovative framework of AI-integrating systems. This framework leverages a modal AI model for real-time disaster forecasting, edge computing for low-latency decision-making, and blockchain for secure, transparent coordination. AIDR-F amalgamates a self-taught AI-based early warning system with continual improvement of the prediction accuracy through ongoing integration of real-time sensor data, satellite imagery, and historical records. Furthermore, an automated response network employing autonomous UAVs, IoT-enabled infrastructure monitoring, and dynamic evacuation route optimisation is also used. It relies on smart contracts that operate on a blockchain’s enabled coordination layer, enabling equitable resource distribution and rapid financial aid deployment. The system incorporates edge AI and equips it to make real-time decisions in a connectivity-constrained environment. Simulation results show that AIDR-F significantly reduces response time, increases preparedness for disasters, and improves coordination efficiency. This research highlights the benefits and applications of an AI-decentralised, community-based disaster management system for mitigating risks and increasing resilience in disaster-prone countries. Work will then be expanded to develop large-scale deployment and policy integration in global disaster management.

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