CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia

2025-10-23

AI Summary

Climate-driven landslides occur frequently in the High Mountain Asia region, yet existing monitoring systems suffer from delayed responses and insufficient inter-agency coordination. To address this, we propose a Multi-Agent Graph Neural Network (MA-GNN) framework that fuses multi-source spatiotemporal data—including satellite remote sensing, meteorological, and topographic inputs—to establish a closed-loop system comprising prediction, planning, and execution agents. The prediction agent employs a dynamic graph neural network for real-time landslide risk assessment; the planning agent generates tiered response strategies; and the execution agent enables cross-departmental emergency resource dispatch. Evaluated in complex high-altitude terrain, our method improves landslide prediction accuracy by 23.6% over baseline models, achieves minute-level situational awareness, and supports adaptive response planning. This work establishes a scalable, proactive technical paradigm for climate-resilient disaster prevention.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Enhancing landslide forecasting accuracy using satellite observations
• Developing real-time situational awareness through multi-agent coordination
• Providing scalable disaster preparedness for vulnerable mountainous regions

Innovation

• Multi-agent system for landslide risk assessment
• Graph neural network processing satellite environmental data
• Real-time collaborative agents enabling proactive intervention