Using PG_TRIGRS with benchmark datasets for landslide susceptibility zonation in central Italy
Authors: Salciarini Diana , Volpe Evelina 
Published: February 2023

The physically-based (PB), probabilistic model PG_TRIGRS (Salciarini et al. 2017) has been applied to obtain landslide susceptibility zonation in central Italy. Input data have been provided by the benchmark datasets released by Alvioli et al. (2003) with a &#8220;Call for collaboration&#8221;.The PB approaches are recognized as effective analytical techniques for landslide susceptibility zonation because they can provide assessments even when there are no past landslide occurrence records, unlike statistical susceptibility analyses (Hwang et al. 2022). Furthermore, they can consider the type, mechanism, and process of landslide occurrence (Chae et al. 2017).PG_TRIGRS has a number of successful applications, mainly in central Italy (Salciarini et al. 2020; Volpe et al. 2021; Volpe et al. 2022). Landslide susceptibility zonation can be obtained starting from a classification of the study area on the basis of a regular square grid. For each cell of the grid, the model requires the definition of: i) geometric characteristics (in terms of elevation and slope); ii) hydro-mechanical characteristics (in terms of soil cohesion, friction angle, and permeability). Due to the generally limited input data and the spatial variability of slope materials when the model is applied over large areas, the predictive capability of the approach involves uncertainty, which is quantified by the probabilistic analysis implemented in the PG_TRIGRS model.In this application, the provided benchmark dataset is subdivided into 2 parts. The model is calibrated using data from the first subset and then used for blind predictions over the second subset. For the calibration, only the information from shallow landslide records is used, since the model is able to predict this type of landslide mechanism. The soil strength parameters are considered random variables with an assigned probability distribution. The stability index of each cell is evaluated in terms of the probability of collapse (PoF) which is a real number ranging from zero to unity, and the uncertainty associated with that.&#160;ReferencesChae BG, Park HJ, Catani F, Simoni A, Berti M (2017) Landslide prediction, monitoring and early warning: a concise review of state-of-the-art. Geosci J 21:1033&#8211;1070.Hwang IT, &#183; Park &#183; HJ, Lee JH (2022) Probabilistic analysis of rainfall&#8209;induced shallow landslide susceptibility using a physically based model and the bootstrap method. Landslides.Salciarini D, Fanelli G, Tamagnini C (2017) A probabilistic model for rainfall-induced shallow landslide prediction at the regional scale. Landslides 14:1731&#8211;1746.Salciarini D, Volpe E, Cattoni E (2020) Probabilistic vs. Deterministic Approach in Landslide Triggering Prediction at Large&#8211;scale. In book: Geotechnical Research for Land Protection and Development, 62-70.Volpe E., Ciabatta L., Salciarini D., Camici S., Cattoni E., Brocca L. (2021). The impact of probability density functions assessment on model performance for slope stability analysis. Geosciences, vol. 11.Volpe E., Gariano S. L., Ardizzone F., Fiorucci F., Salciarini D. (2022). A Heuristic Method to Evaluate the Effect of Soil Tillage on Slope Stability: A Pilot Case in Central Italy. LAND, vol. 11.