Landslide prediction system for rainfall induced landslides in Slovenia (Masprem)

• Main Authors: Mateja Jemec Auflič, Jasna Šinigoj, Matija Krivic, Martin Podboj, Tina Peternel, Marko Komac
• Format: Article
• Language: English
• Published: Geological Survey of Slovenia 2016-12-01
• Series: Geologija
• Subjects: shallow landslides; prediction; hazard; validation
• Online Access: http://www.geologija-revija.si/dokument.aspx?id=1287
• ISSN: 0016-7789; 1854-620X

In this paper we introduce a landslide prediction system for modelling the probabilities of landslides through time in Slovenia (Masprem). The system to forecast rainfall induced landslides is based on the landslide susceptibility map, landslide triggering rainfall threshold values and the precipitation forecasting model. Through the integrated parameters a detailed framework of the system, from conceptual to operational phases, is shown. Using fuzzy logic the landslide prediction is calculated. Potential landslide areas are forecasted on a national scale (1: 250,000) and on a local scale (1: 25,000) for fie selected municipalities where the exposure of inhabitants, buildings and different type of infrastructure is displayed, twice daily. Due to different rainfall patterns that govern landslide occurrences, the system for landslide prediction considers two different rainfall scenarios (M1 and M2). The landslides predicted by the two models are compared with a landslide inventory to validate the outputs. In this study we highlight the rainfall event that lasted from the 9th to the 14th of September 2014 when abundant precipitation triggered over 800 slope failures around Slovenia and caused large material damage. Results show that antecedent rainfall plays an important role, according to the comparisons of the model (M1) where antecedent rainfall is not considered. Although in general the landslides areas are over-predicted and largely do not correspond to the landslide inventory, the overall performance indicates that the system is able to capture the crucial factors in determining the landslide location. Additional calibration of input parameters and the landslide inventory as well as improved spatially distributed rainfall forecast data can further enhance the model's prediction.