[{"data":1,"prerenderedAt":65},["ShallowReactive",2],{"news-hkust-researchers-s2s-hazardagent-shortlisted-among-top-20-in-mazu-global-intelligence-innovation-application-challenge":3},{"id":4,"title":5,"body":6,"cover":52,"date":53,"description":12,"extension":54,"link":55,"meta":56,"navigation":57,"newsId":58,"path":59,"seo":60,"slug":61,"source":62,"stem":63,"__hash__":64},"news\u002Fnews\u002F59-hkust-researchers-s2s-hazardagent-shortlisted-among-top-20-in-mazu-global-intelligence-innovation-application-challenge.md","HKUST Researchers’ S2S-HazardAgent Shortlisted Among Top 20 in MAZU Global Intelligence Innovation Application Challenge",{"type":7,"value":8,"toc":48},"minimark",[9,13,16,19,27,40],[10,11,12],"p",{},"S2S-HazardAgent, developed by CCRS Director Prof. LU Mengqian’s team in collaboration with CCRS member Prof. YUAN Binhang’s group at HKUST, was shortlisted among the Top 20 application scenarios in the MAZU Global Intelligence Innovation Application Challenge on September 10, 2026. With LI Chenyue and DENG Wen serving as project co-leads, the system aims to bridge the gap between scientific forecasts and actionable climate risk management.",[10,14,15],{},"S2S-HazardAgent integrates multi-source climate and disaster data with AI agent orchestration to support subseasonal-to-seasonal multi-hazard analysis and early-warning services. Its capabilities include evidence-grounded reasoning, spatiotemporal analysis, compound hazard diagnosis, and automated early-warning report generation, translating complex climate information into actionable decision support.",[10,17,18],{},"On July 17, 2026, Prof. LU delivered a keynote presentation at the World Artificial Intelligence Conference (WAIC 2026), titled “Mazu Global Digital Intelligence Innovation Application Case: AI Agent Orchestration for Multi-Hazard Subseasonal-to-Seasonal Risk Analysis & Early-Warning Support.” The presentation highlighted the development of S2S-HazardAgent in Brazil under the SEPRESS initiative, representing MAZU’s first application in South America through South-South cooperation. The showcased system demonstrates potential applications across disaster management, agriculture, and energy sectors.",[10,20,21,22],{},"System Demo: ",[23,24,25],"a",{"href":25,"target":26},"https:\u002F\u002Fdisaster-database-v3.vercel.app\u002F","_blank",[28,29,30,31,30,36],"figure",{},"\n  ",[32,33],"img",{"src":34,"style":35},"https:\u002F\u002Fmedia.meteonex.hkust.edu.hk\u002Fportals\u002FCCRS\u002Fpublic\u002Fimages\u002Fnews\u002Fmazu1.png","width: 80%",[37,38,39],"figcaption",{},"\n  Figure 1. S2S-HazardAgent platform homepage. The multilingual AI-powered platform for subseasonal-to-seasonal multi-hazard risk analysis and early-warning support, developed for climate resilience applications in Brazil.\n  ",[28,41,30,42,30,45],{},[32,43],{"src":44,"style":35},"https:\u002F\u002Fmedia.meteonex.hkust.edu.hk\u002Fportals\u002FCCRS\u002Fpublic\u002Fimages\u002Fnews\u002Fmazu2.png",[37,46,47],{},"\n  Figure 2. S2S-HazardAgent interactive analysis interface. The integrated system combines disaster event exploration (left), geospatial visualization and event details (center), and an AI agent assistant (right) to support evidence-based multi-hazard analysis and early-warning decision-making.\n  ",{"title":49,"searchDepth":50,"depth":50,"links":51},"",2,[],"https:\u002F\u002Fmedia.meteonex.hkust.edu.hk\u002Fportals\u002FCCRS\u002Fpublic\u002Fimages\u002Fnews\u002Fcover\u002Fid59.png","2026-10-21","md",null,{},true,59,"\u002Fnews\u002F59-hkust-researchers-s2s-hazardagent-shortlisted-among-top-20-in-mazu-global-intelligence-innovation-application-challenge",{"title":5,"description":12},"hkust-researchers-s2s-hazardagent-shortlisted-among-top-20-in-mazu-global-intelligence-innovation-application-challenge","local","news\u002F59-hkust-researchers-s2s-hazardagent-shortlisted-among-top-20-in-mazu-global-intelligence-innovation-application-challenge","UZURqX4ojNAzHPpe6JjYUe_5qYWd8E-1FgQvZfpcGTI",1792997702848]