Generative AI-Driven Disaster Management and Alert System Leveraging Multi-Source Data Fusion

Abstract

These constitute the most salient issues in disaster preparedness which are rapid evaluation of the dangers and judicious application of precautionary warnings. Traditional models, that utilise the physical implementation of the sensor webs and that typically use inflexible threshold logic, typically lack the structuring to query dynamic situational contexts or give results in a subtle way of interpretation. This paper will present a softwarefocused model of disaster management wherein me-teorological, media, crowdsourced social, and archival coverage of the disaster are combined into an integrated analysis channel. The scoring scheme is deterministic and produces a Composite Risk Index by the algorithmic combination of all streams of data, a big language model then provides post-hoc, natural language explanations in order to justify danger ratings. The modern risk environments are modelled in the interactive dashboard which is designed based on latest web-based technologies and sends directive advisories. Simulations of multi-hazard scenarios with the use of empirical trials identify a classification accuracy of 91.4 percent and end-to-end processing time that is an average of twelve seconds, thus proving the applicability of the system in operational conditions limited by small resources, damaged infrastructure, or inaccurate sensors.