A GeoAI-Driven and Decision-Oriented Methodology for Multi-Hazard Early Warning System Development

Abstract Multi‑hazard early warning systems (MHEWS) are widely recognized as one of the most cost‑effective measures for disaster risk reduction and climate adaptation to date. However, intelligent solutions that can dynamically adapt to the heterogeneous disaster risk and impact information requirements of multi‑departmental stakeholders remain scarce. To facilitate a shift from quantity expansion to quality optimization, this study proposed a methodology for MHEWS development from four perspectives: theoretical foundations, operational procedures, algorithmic technologies, and information hubs. The methodology enhances decision support effectiveness through geospatial artificial intelligence (GeoAI). Specifically: (1) Multi‑party collaboration within MHEWS is redefined as GeoAI Agents, enabling customized intelligent services. (2) An evidence‑based early warning decision support mechanism is constructed through the identification of early warning information requirements. (3) Using a multi‑agent deep reinforcement learning algorithm, the system is co‑driven by data and knowledge, adapts to evolving risks, and achieves end‑to‑end integration and training. (4) A geographic knowledge graph is constructed to consolidate and transform multi-dimensional information flows into actionable insights, integrating large language models to enable hybrid reasoning. Together, these methodology components aim to enable the process of spatiotemporal data → risk information → policy knowledge → practical actions. We develop a subdistrict-level prototype (Smart Early Warning Town) as a validation case and conduct empirical evaluation across seven modules. The results assess both the opportunities and barriers of implementing the proposed methodology and outline an initial pathway for leveraging intelligent approaches to bridge the gap between global high-level visions and routine operational practice.