A Dynamic Landslide Warning Model Based on Grey System Theory

Landslides are among the most frequent and destructive geological disasters, causing significant loss of life and property. Research on landslide prediction is crucial for minimizing disaster-related losses, ensuring public safety, and developing reliable early warning systems and preparedness strategies. Existing prediction technologies are broadly classified into mechanical modeling methods and mathematical modeling methods. Compared to mechanical modeling, mathematical modeling relies on data analysis, enabling the use of existing monitoring data without requiring detailed geological information. Among these, the grey model stands out for its high accuracy with minimal information requirements, as well as its simplicity and ease of implementation. Based on these advantages, grey model-based landslide prediction has emerged as a key research focus. This paper proposes a dynamic landslide warning model that integrates the concept of risk with grey system theory, using a small amount of real-time monitoring data. Experimental results demonstrate the model’s effectiveness in accurately predicting landslides and providing timely warnings for unstable slopes.