Real-Time Multi-Scale Slope Stability Forecasting

Landslides affecting natural and engineered slopes pose a growing challenge for disaster risk reduction, particularly under the increasing frequency and intensity of rainfall and snowmelt events driven by climate change. Operational slope stability forecasting requires the integration of meteo-hydro-geological data sources, physical understanding of failure mechanisms, and frameworks capable of delivering timely predictions. This abstract summarizes our research activities of creating an integrated real-time cloud-based operational framework that combines slope-and regional-scales digital twins for landslide forecasting, leveraging real-time monitoring, numerical modelling, and data-driven methods.

At the regional scale, slope stability forecasting is addressed through a hybrid methodology that merges physically-based infinite slope models with data-driven landslide susceptibility and probability models (Abraham et al., 2025). The regional framework operates across first-order catchments within a selected study area in Norway. A physically-based model computes pixel-wise Factor of Safety (FoS) values using precipitation, topography, and subsurface parameters, calibrated through back-analysis and applied in forward forecasting model. In parallel, a machine learning data-driven model estimates the probability of landslide occurrence at the catchment scale. Both model types are deployed as automated cloud services that generate daily forecasts, overcoming key operational challenges related to model integration, parameter updating, and large-scale data handling. Forecast outputs are disseminated through NGI Live, the Norwegian Geotechnical Institute’s data platform, supporting Landslide Early Warning Systems (LEWS).

Complementing the regional framework, slope-scale forecasting is achieved through the development of a digital twin of an instrumented slope in Norway (Piciullo et al., 2022; Piciullo et al., 2025). The digital twin integrates real-time monitoring of hydrological variables, such as volumetric water content and pore water pressure, with numerical slope stability modelling and machine learning. The numerical model is continuously validated against monitored data and used to calculate the FoS. To enable efficient operational forecasting, data-driven models, including Polynomial Regression and Random Forest, are trained on simulated FoS values, monitored hydrological conditions, and meteorological inputs to forecast the rolling three days FoS. These data-driven models replace the computationally intensive numerical model within the cloud service, enabling rapid and reliable FoS forecasts. A performance evaluation demonstrates that the data-driven surrogates provide accurate and robust FoS predictions comparable to the numerical model, highlighting their suitability for operational early warning applications.

By integrating detailed slope-scale digital twins with scalable regional-scale forecasting, we illustrates a coherent multi-scale approach to landslide prediction. The proposed framework is readily transferable to other sites and regions, offering a practical pathway for enhancing real-time landslide early warning and risk management.

The authors gratefully acknowledge the support received from The HuT EU project (ID101073957, https://thehut-nexus.eu/), which played a crucial role in facilitating and advancing our research.

 

References

Abraham, M. T., Piciullo, L., Liu, Z., Drøsdal, et al. (2025). Operational regional scale landslide forecasts: Physics-based and data-driven models. Proceedings of the 9th International Symposium on Geotechnical Safety and Risk (ISGSR 2025). Research Publishing, Singapore. https://doi.org/10.3850/981-973-0000-00-0-isgsr2025-paper.

Piciullo, L., Abraham, M. T., Drøsdal, I. N., and Paulsen, E. S. (2025). An operational IoT-based slope stability forecast using a digital twin. Environ. Model. Softw. 183, 106228. https://doi.org/10.1016/j.envsoft.2024.106228.

Piciullo, L., Capobianco, V., and Heyerdahl, H. (2022). A first step towards a IoT-based local early warning system for an unsaturated slope in Norway. Nat. Hazards 114. https:// doi.org/10.1007/s11069-022-05524-3.