用多智能体系统提升高山地区滑坡风险预测精度
CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia
- 构建预测、规划、执行三智能体协同框架
- 融合卫星与环境数据实现实时风险评估
- 适合灾害预警与山区应急决策人员使用
滑坡是气候变化加剧下的严重灾害，尤其在高亚洲地区影响深远。尽管卫星和时序数据日益丰富，但及时检测与响应仍显滞后且分散。本文提出CC-GRMAS，一个基于多智能体的图神经网络系统，整合多种卫星观测与环境信号，提升滑坡预测准确性。系统由预测、规划、执行三个互连智能体构成，协同实现实时态势感知、响应规划与干预行动。通过引入局部环境因素并实现多智能体协调，该方法为脆弱山地地区提供了可扩展、主动的气候韧性灾备方案。
原文摘要 · Abstract (English)
Landslides are a growing climate induced hazard with severe environmental and human consequences, particularly in high mountain Asia. Despite increasing access to satellite and temporal datasets, timely detection and disaster response remain underdeveloped and fragmented. This work introduces CC-GRMAS, a framework leveraging a series of satellite observations and environmental signals to enhance the accuracy of landslide forecasting. The system is structured around three interlinked agents Prediction, Planning, and Execution, which collaboratively enable real time situational awareness, response planning, and intervention. By incorporating local environmental factors and operationalizing multi agent coordination, this approach offers a scalable and proactive solution for climate resilient disaster preparedness across vulnerable mountainous terrains.
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