IoT-based monitoring and modelling of an unsaturated slope in Norway
Authors: Piciullo Luca , Capobianco Vittoria , Heyerdahl Håkon 
Published: February 2023

A framework for an IoT-based local landslide early warning system (Lo-LEWS) has been proposed in Piciullo et al., 2022. The framework is composed by four main components: monitoring, modelling, forecasting and warning. The first two phases have been applied to capture the hydrological behavior and compute the slope stability of a natural unsaturated slope located adjacent to a railway track in Eastern Norway. The steep slope (about 45&#176; in the upper part) is instrumented with several sensors since 2016 (Heyerdahl et al., 2018). Volumetric water content (VWC) and pore-water pressure (PWP) sensors were installed in late spring of 2016. In 2022 a weather station has been added to the monitoring network for measuring climate variables. These data are collected in real-time and are accessible on internet, while the PWP from the electric piezometers are collected manually.GeoStudio software was used to create a slope model able to replicate the in-situ monitored conditions. SEEP module was used to back calculate the observed VWC and PWP. Simulations were carried out by changing the initial and boundary climate conditions of the slope. Two main simulation sets were conducted considering: an initial calibration of VWC profile (C), no calibration (NC). For each one, three different surface boundary conditions were applied: i) only precipitation, ii) precipitation and atmospheric conditions, iii) precipitation, atmospheric conditions and vegetation, considering the Penman-Monteith equation for evapotranspiration. The simulations have been validated using Taylor diagrams that graphically summarize how closely a pattern, or a set of patterns, matches observations. The results show that including an initial calibration, climate variables and vegetation, is crucial to best model the response of the unsaturated slope in Eidsvoll.A sensitivity analysis on the hydraulic conductivity and the permeability anisotropy contributed to better define the input data and to improve the fitting model-observations. The effectiveness of the best simulation, in back-calculating VWC, was tested for 3 different time periods: 6-month, 1-year, 1.25-year. The results show that the hydrological model can adequately represent the real monitored conditions up to a 1-year period, a recalibration is needed afterward. In addition, a slope stability analysis with GeoStudio SLOPE module, for the 1-year period, was coupled to the SEEP module (Piciullo et al., 2022) to compute the factor of safety (FS). A supervised, regression machine learning analysis has been carried out using a random forest machine learning model. The analysis has highlighted the importance of the monitored VWC in forecasting the FS. The VWC values are the variables measured in real time on the slope. For this reason, the possibility to predict the FS from VWC is relevant for the implementation of a real-time slope stability analysis as a Lo-LEWS. &#160;Heyerdahl H., Hoydal O. A., Kvistedal Y., Gisnas K. G., Carotenuto P. (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.Piciullo, L., Capobianco, V. & Heyerdahl, H. (2022) A monitored unsaturated slope in Norway: Eidsvoll case study. Klima 2050 Report;35 https://hdl.handle.net/11250/3000249 &#160;&#160;