用智能代理系统实现暴雨预警与响应联动，提升灾害应对效率。
Agentic AI Framework for Cloudburst Prediction and Coordinated Response
- 构建多智能体协同框架，整合感知、预测到响应全流程。
- 在巴基斯坦北部验证，预警准确率与提前量显著优于基线模型。
- 适合应急决策、气象预警与气候韧性建设领域应用。
传统预报系统将预测与响应割裂，难以应对如暴雨等极端短时降雨事件。本文提出一种智能代理人工智能系统，融合传感、预报、降尺度、水文建模与协同响应，形成闭环的实时决策体系。系统采用自主协作的智能体，在事件全周期内推理、感知与行动，利用气象预测智能转化为实时决策能力。基于多年雷达、卫星与地面数据对巴基斯坦北部的评估显示，该多智能体配置显著提升了预报可靠性、关键成功指数与预警提前时间。通过通信与路径规划智能体最大化受威胁人口覆盖范围，降低疏散误差；嵌入式学习层提供自适应校准与透明可审计性。结果表明，协同智能体能将大气数据流转化为可操作的前瞻性洞察，为可扩展、自适应、学习型气候韧性提供平台。
原文摘要 · Abstract (English)
The challenge is growing towards extreme and short-duration rainfall events like a cloudburst that are peculiar to the traditional forecasting systems, in which the predictions and the response are taken as two distinct processes. The paper outlines an agentic artificial intelligence system to study atmospheric water-cycle intelligence, which combines sensing, forecasting, downscaling, hydrological modeling and coordinated response into a single, interconnected, priceless, closed-loop system. The framework uses autonomous but cooperative agents that reason, sense, and act throughout the entire event lifecycle, and use the intelligence of weather prediction to become real-time decision intelligence. Comparison of multi-year radar, satellite, and ground-based evaluation of the northern part of Pakistan demonstrates that the multi-agent configuration enhances forecast reliability, critical success index and warning lead time compared to the baseline models. Population reach was maximised, and errors during evacuation were minimised through communication and routing agents, and adaptive recalibration and transparent auditability were provided by the embedded layer of learning. Collectively, this leads to the conclusion that collaborative AI agents are capable of transforming atmospheric data streams into practicable foresight and provide a platform of scalable adaptive and learning-based climate resilience.
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