Conversational AI for Slope Stability Monitoring: Enabling “Chat-with-Your-Data” as a Decision Support Tool

The growing availability of IoT-enabled sensor networks has transformed how slope stability is monitored within Landslide Early Warning Systems (LEWS), producing vast datasets on pore pressures, groundwater levels, displacements, and external drivers such as rainfall. In the literature, there is an increased trend to apply advanced data analysis approaches and surrogate models to support slope stability assessment and early warning. However, the sheer volume and complexity of these data often limit users’ ability to interact with them in a flexible and intuitive way. Emerging advances in multi-modal generative AI models and agentic frameworks suggest a new paradigm: chat-with-your-data.

In this approach, users interact directly with slope monitoring data through natural language, requesting tailored visualizations, summaries, analyses, or forecasts without the need for bespoke coding or rigid workflows. In the context of slope stability assessment and early warning, a practitioner could ask for recent pore pressure trends, rainfall-displacement correlations, threshold exceedances, or anticipated changes in stability conditions based on forecasted meteorological inputs for a specific site. The system identifies the relevant data sources, retrieves data, performs the required operations, and returns insights in user-friendly formats such as maps, diagrams, or downloadable datasets.

The potential benefits include more direct access to relevant data and analyses, uncovering correlations, and enabling real-time decision support. However, challenges remain. These include ensuring that project-level access controls are respected, handling heterogeneous geospatial references, providing tailored data representations across spatial scales, and maintaining transparency and reliability in automatically generated outputs. Addressing these issues requires combining domain-specific knowledge in slope stability and landslide processes with expertise in generative AI and data governance.

This work outlines a vision for how conversational interfaces could enhance slope-scale Landslide Early Warning Systems by supporting monitoring, modelling, and forecasting activities through intuitive human–data interaction. By allowing experts to query their data directly, we move toward systems that are more adaptable, interpretable, and insight-driven, promoting more effective use of monitoring data for targeted warning and risk mitigation.