Landslides, a prominent geohazard, cause considerable disturbances in many natural terrains, impacting both ecosystems and human habitats. In recent years, the intervention of climatic and tectonic activities has increased the frequency of such hazards. Although numerous methodologies have been developed to analyse landslide susceptibility, there remains a pronounced gap in probabilistic slope stability techniques incorporating rainfall infiltration models on a regional scale. The study proposes a new tool TRIGRS-FOSM (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability-First Order Second Moment) developed to account for the uncertainties in soil shear strength properties along with the effect of vegetative cover using probabilistic infinite slope stability analysis. This user-friendly tool is seamlessly integrated with the infiltration model of TRIGRS and adaptable with Geographic Information Systems (GIS), enabling assessment across larger regions. It is especially tailored for regions prone to rainfall-induced landslides such as the Western Ghats of India, which has been under persistent threat due to increasing rainfall. This paper aims to validate the efficacy of TRIGRS-FOSM in the Western Ghats, contrasting it with traditional methodologies and focusing on the landslide prediction accuracy, especially within the Wayanad and Idukki districts of Kerala. On verifying TRIGRS-FOSM against Monte Carlo Simulations, it was observed that TRIGRS-FOSM exhibited a lower relative error for typical ranges of variability associated with soil material properties, underlining its enhanced reliability. Furthermore, the probabilistic approach showcased improvements over the deterministic method, elevating the prediction accuracy by 10% in Wayanad and 14% in Idukki districts based on their AUROC values. Through TRIGRS-FOSM, this work intends to provide a computationally efficient method to account uncertainties of landslide susceptibility assessment, thereby making a substantial contribution to geohazard management.

The influence of a firm stratum on the stability of a slope under undrained conditions has long been of interest to geotechnical investigators, which has been studied in a number of previously important works in relation to slope stability analyses without considering soil spatial variability. This paper proposes another look at such a problem in the context of probabilistic slope stability analyses considering soil spatial variability. Here, the random field (RF) is used to simulate the spatially variable undrained soil strength. It is found that under stationary RF and non-stationary RF with the soil strength at the top ground surface (s(u0)) larger than 0, the depth of the firm stratum (H-f) has a significant influence on the mean and standard deviation of factor of safety (i.e., mu [FS] and 6 [FS], respectively). By contrast, under non-stationary RF with s(u0) = 0, H-f has a slight influence on mu [FS], but its influence on 6 [FS] is non-negligible. In addition, the autocorrelation distance is found to have an insignificant impact on the influential effect of H-f f on mu [FS]. However, for 6 [FS], this impact is not negligible. When the autocorrelation distance is smaller, the influence of Hf f on 6 [FS] would be more significant. Under non-stationary RF, the influence of H-f on 6 [FS] would be slight if the autocorrelation distance is large enough. Furthermore, the impacts of slope ratio, su0, u0 , isotropic and anisotropic features on the influential effects of H-f are also investigated and discussed.