Abstract
Landslide susceptibility assessment over large areas is considered a preliminary step for the planning or design of the most appropriate risk mitigation measures. The use of physically based models is considered a useful tool for landslide susceptibility assessment. Sometimes, using the available geotechnical input data, physically based models can be used to assess landslide susceptibility to obtain a susceptibility map which allows the expert to identify areas where detailed in situ investigations and laboratory tests should be carried out. In this context, the paper proposes a methodology based on the use of TRIGRS to assess landslide susceptibility in an area of about 1 km2 frequently affected by shallow phenomena in weathered gneiss. Owing to the fact that these materials are extremely complex to characterize from a mechanical and hydraulic point of view, the methodology starts with the collection and analysis of the geotechnical data available for weathered gneiss outcrop** in the study area. These data are combined with the data provided by scientific literature on soils similar, for genesis and stress history, to those of the studied area. Through the application of TRIGRS, the data are combined in order to obtain the values of parameters that better analyze shallow landslide source areas. Subsequently, using the abovementioned values, several susceptibility maps are obtained. Finally, the most representative shallow landslide susceptibility map for the area is chosen by means of the error index (EI), the true positive fraction (TPF), and the forecasting index (FI). The success of the best map is confirmed by the high value of the area under the receiver operator characteristic curve (AUC) that demonstrates a good level of forecasting ability.
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Ciurleo, M., Mandaglio, M.C. & Moraci, N. Landslide susceptibility assessment by TRIGRS in a frequently affected shallow instability area. Landslides 16, 175–188 (2019). https://doi.org/10.1007/s10346-018-1072-3
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DOI: https://doi.org/10.1007/s10346-018-1072-3