A Multimodal Multi-Agent Framework for Automated Landslide Risk Management

Landslides are among the most destructive geological hazards, requiring rapid, accurate, and comprehensive risk assessment to minimize loss of life and property. Traditional management systems often struggle to integrate heterogeneous data sources—such as real-time environmental metrics and unstructured historical records—resulting in delayed decision-making. To address this challenge, this paper proposes a novel multi-agent system framework designed for automated landslide risk management and emergency response. The proposed framework orchestrates three specialized agents to achieve a holistic understanding of disaster risks. The first agent, the Data Processing Agent, is responsible for the real-time acquisition of IoT data, specifically rainfall intensity. It utilizes embedded AI algorithms to process this time-series data and compute instantaneous landslide probability. The second agent, the Contextual Retrieval Agent, leverages Retrieval-Augmented Generation (RAG) technology. It retrieves and synthesizes relevant historical landslide documentation and multi-modal geological reports, providing a qualitative context to the quantitative data. The third agent, the Decision and Planning Agent, functions as the central reasoning unit. It fuses the probabilistic outputs from the first agent and the historical context provided by the second agent. Based on this multi-modal synthesis, the agent determines the current disaster risk level and automatically generates targeted evacuation plans for residents in affected areas. Experimental validation demonstrates the efficacy of this multi-modal framework in complex disaster scenarios. The system achieved a 30% improvement in response speed compared to traditional methods. Furthermore, the framework successfully realized a fully automated workflow from data acquisition to strategic planning, significantly enhancing the reliability and timeliness of landslide disaster management.