The Generative Logic and Implementation Pathways of AI-Empowered Precision Enhancement in Disaster Monitoring and Forecasting

Abstract

The increasing frequency of extreme weather events and compound disasters has rendered traditional disaster monitoring and forecasting models inadequate for meeting complex operational demands. Artificial intelligence (AI) has emerged as a critical tool for enhancing the precision of disaster forecasting. To elucidate the mechanisms through which AI empowers forecasting precision and to propose feasible optimization pathways, this paper integrates existing research with domestic practical cases. It deconstructs the rich connotations of forecasting precision across three dimensions—spatiotemporal, intensity, and impact—and analyzes the generative logic of AI empowerment from value, technological, and institutional perspectives, while also examining its practical boundaries. The study reveals that AI-driven improvements in forecasting effectiveness do not rely solely on technological upgrades, but rather constitute a governance process involving the synergistic coupling of technology, institutions, and grassroots capacity. Accordingly, this paper proposes countermeasures including the establishment of unified data platforms, the construction of human-AI interactive systems, the localization of AI models, and the refinement of a“peacetime-wartime integrated”emergency technology support system, thereby providing theoretical underpinnings for the modernization of emergency management with Chinese characteristics.