A fully operational IoT-based slope stability analysis for an unsaturated slope in Norway

The framework proposed by Piciullo et al., 2022 for a Internet of Things (IoT)-based local landslide early warning system (Lo-LEWS) consists of four main components: monitoring, modelling, forecasting, and warning. It was applied to a steep natural slope in Norway, equipped with various hydrological and meteorological sensors since 2016. Volumetric water content (VWC) and pore-water pressure (PWP) sensors were installed in 2016 (Heyerdahl et al., 2018). A weather station was added in 2022 to measure climate variables: rainfall, relative humidity, wind speed, air temperature among others. The sensors and weather station regularly send data to NGIs IoT data platform (NGI Live), which stores and makes the data available real-time through online dashboards and Application Programming Interface (API). GeoStudio software was used to create a reliable digital twin of the slope with the aim of back-calculating the in-situ hydrological conditions. Calibration, climate variables, and vegetation proved crucial for accurately modelling the slope's response . Sensitivity analysis on hydraulic conductivity and permeability anisotropy improved input data and model fitting. The hydrological model adequately represented monitored conditions up to a 1-year period (Piciullo et al., 2022). 

A fully operational IoT-based slope stability analysis has been recently established. The digital twin model has been used to evaluate the slope stability (i.e., factor of safety, FS) coupling SEEP and Slope analyses for 5 different 1-year datasets. Both past and future scenarios have been considered:  2019-2020, 2021-2022, 2022-2023, 2064-2065, 2095-2096. The inputs (i.e., hydrological and weather variables) and the FS results have been used to train different machine learning and statistical models. The feature considered are VWC, PWP, rainfall, temperature, LAI; the target was the FS. The best models able to predict the FS, given the features, are polynomial regression and random forest.

In order to predict the FS for the upcoming three days, PASTAS model (Collenteur et al., 2019) and the Norwegian Meteorological Institute webpage have been used to respectively forecast the hydrological variables (i.e., VWC and PWP) and rainfall, air temperature and relative humidity data. We created a web service that once a day automatically (1) fetches measured data from NGI Live using the NGI Live API, (2) runs predictions for the next three days based on the measured data, (3) sends the predicted values back to NGI Live, making them available for real-time visualization in online dashboards. This case study can be seen as a fully operational example of the use of IoT and digital twinning to provide a real-time stability assessment for a slope as well as a collaborative effort among different expertise: geotechnical, hydrological, instrumental and informatics.  

REFERENCES

Heyerdahl H., et al. (2018). Slope instrumentation and unsaturated stability evaluation for steep natural slope close to railway line. In UNSAT 2018: The 7th International Conference on Unsaturated Soils.

Collenteur R. A., et al. (2019). Pastas: Open Source Software for the Analysis of Groundwater Time Series. Groundwater, 57(6):877–885. URL: https://doi.org/10.1111/gwat.12925, doi:10.1111/gwat.12925.

Piciullo, L., et al. (2022) A first step towards a IoT-based local early warning system for an unsaturated slope in Norway. Nat Hazards 114, 3377–3407 (2022). https://doi.org/10.1007/s11069-022-05524-3