A Straightforward Integrated Assessment of Landslide Initiation, Potential Process Paths, and Exposure in Tbilisi, Georgia

Landslides pose a considerable threat to urban environments, and understanding where and how they may impact critical infrastructure is essential for risk management and early warning. This study presents an integrated methodological framework for data-driven landslide analysis in Tbilisi, Georgia, combining initiation susceptibility mapping, empirical runout path assessment, and exposure analysis. The approach focuses on the Tbilisi area (~505 km²) and first models landslide initiation susceptibility separately for slides, flows, and falls using a range of topographic and geological predictors. Generalized Additive Models (GAMs) were applied to produce continuous probability maps of initiation, which were subsequently classified into low, medium, and high susceptibility classes to define potential source locations for process path simulations. Based on these release locations, potential downslope propagation was estimated using a simplified, empirical energy-line approach based on the angle-of-reach principle. Multiple stochastic simulations per release cell captured variability in runout paths. The resulting potential process path maps then formed the basis for exposure assessment by intersecting them with spatial data on buildings, roads, and railway lines. The analysis identifies areas most likely to be impacted, providing an evaluation of multi-landslide exposure across the area. Beyond serving as a baseline for spatial planning, the results are being evaluated for integration with real-time meteorological nowcasting products to support impact-based early warning. Overall, the study demonstrates the potential of a straightforward landslide modelling chain to support risk management and early warning, contributing to enhanced resilience in Tbilisi. The analysis was conducted within the MedEWSA project funded by Horizon Europe (Grant Agreement No. 101121192).