灾害应对需强化态势感知,以应对未知风险和复杂危机。
Situational Awareness as the Imperative Capability for Disaster Resilience in the Era of Complex Hazards and Artificial Intelligence
- 构建技术-流程-人员协同的态势感知体系
- 实现多源数据融合与实时危机预判
- 适合应急管理和智能决策研究者参考
灾害常超出既有模型预测,暴露出未预见的影响与脆弱点,影响有效响应。本文强调,态势感知(SA)——即对动态危机状况的感知、解读与预判能力——是提升灾害韧性的关键却常被忽视的能力。尽管风险缓解可降低已知威胁,但并非所有灾害可消除;真正的适应性韧性取决于组织能否快速发现新兴故障、整合多元数据,并精准部署干预措施。本文提出技术-流程-人员一体化路线图,展示实时灾害预警、可互操作的工作流与赋能团队如何将原始数据转化为行动洞察。系统化方法支持联邦式数据所有权与模块化分析,使多个机构在不牺牲各自运作模式的前提下共享及时信息。同样重要的是,结构化的认知处理流程与认知负荷防护机制有助于人在数据过载中保持高效决策。将态势感知视为社会技术核心而非附加功能,凸显其作为灾害韧性核心目标的紧迫性。最后提出未来研究方向:开发态势感知度量指标、设计可信的人机协作机制、强化包容性数据治理,确保社区具备应对预期与突发危机的能力。
原文摘要 · Abstract (English)
Disasters frequently exceed established hazard models, revealing blind spots where unforeseen impacts and vulnerabilities hamper effective response. This perspective paper contends that situational awareness (SA)-the ability to perceive, interpret, and project dynamic crisis conditions-is an often overlooked yet vital capability for disaster resilience. While risk mitigation measures can reduce known threats, not all hazards can be neutralized; truly adaptive resilience hinges on whether organizations rapidly detect emerging failures, reconcile diverse data sources, and direct interventions where they matter most. We present a technology-process-people roadmap, demonstrating how real-time hazard nowcasting, interoperable workflows, and empowered teams collectively transform raw data into actionable insight. A system-of-systems approach enables federated data ownership and modular analytics, so multiple agencies can share timely updates without sacrificing their distinct operational models. Equally crucial, structured sense-making routines and cognitive load safeguards help humans remain effective decision-makers amid data abundance. By framing SA as a socio-technical linchpin rather than a peripheral add-on, this paper spotlights the urgency of elevating SA to a core disaster resilience objective. We conclude with recommendations for further research-developing SA metrics, designing trustworthy human-AI collaboration, and strengthening inclusive data governance-to ensure that communities are equipped to cope with both expected and unexpected crises.
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