Application of beta regression for the prediction of landslide areal density in South Tyrol, Italy 

Abstract

The concept of landslide hazard entails evaluating landslide occurrence in space (i.e., where landslides may occur), in time (i.e., when or how often landslides may occur), and their intensity (i.e., how destructive landslides may be). At regional scales, data-driven methods are implemented to separately analyze the spatial component (i.e., landslide susceptibility) and the temporal conditions leading to landslide occurrence, such as rainfall thresholds. However, assessing how large a landslide may develop once triggered is seldom conducted and poses a persistent challenge to satisfying the complete definition of landslide hazard. So far, only a few publications have addressed this issue by predicting the total areal extent of landslides based on certain mapping units, such as slope units. Limitations arise since the total areal extent of landslides within a mapping unit is strongly influenced by the size of the mapping unit, leading to larger mapping units being more likely to encompass larger total landslide areas. To tackle these challenges, this study aims to predict the landslide area proportion per slope unit in South Tyrol, Italy (7,400 km²). Our approach built upon past landslide occurrences from 2000 to 2020, systematically related to damage-causing and infrastructure-threatening landslide events. The method involved delineating slope units, filtering the landslide inventory, designing the sampling strategy, removing trivial areas, and aggregating the environmental variables (e.g., topography, lithology, land cover, and precipitation) to the slope unit partition. We tested a generalized additive beta regression model to estimate statistical relationships between the various static predictors and the target landslide areal density. The resulting spatially explicit predictions are evaluated through cross-validation from multiple perspectives. Applications and shortcomings of the approach are discussed. The proposed method is anticipated to provide valuable insights and alternatives to assessing landslide intensity and moving toward landslide hazard in a data-driven context. The outcomes associated with this research are framed within the PROSLIDE project, which has received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige.