An operational IoT-based slope stability forecast using a digital twin
Citation
Piciullo, L., Abraham, M.T., Drøsdal, I.N., Paulsen, E.S. (2025). An operational IoT-based slope stability forecast using a digital twin. Environmental Modelling & Software, 183: 106228. Link to paper
Abstract
This study develops an automated IoT-based real-time slope stability analysis that integrates real-time hydrological monitoring, modelling and data-driven approaches for slope stability forecasting. The analyses were conducted for 5 different 1-year datasets encompassing both historical (2019–2020, 2021–2022, 2022–2023) and future projections (2064–2065, 2095–2096) of meteorological variables. Daily variation of hydrological and meteorological variables, along with vegetation indicators were used as inputs to train data-driven models, using polynomial regression (PR) and Random Forest (RF), to forecast daily factor of safety values. The trained models proved to be effective and were employed to forecast slope stability for the rolling three days. The hydrological variables are forecasted using an open-source Python package called Pastas, which uses historical and forecasted meteorological and vegetation conditions to replicate and forecast the time series of volumetric water content (VWC) and pore water pressure (PWP). This framework creates a digital twin of the slope, enabling real-time stability assessment and early warning capabilities.