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.

Accurate prediction of landslide movement is essential for effective disaster prevention and control. However, current studies on probabilistic large deformation analysis of landslides assume transverse anisotropy of soil, overlooking the impact of the soil fabric and depositional orientation on the post-failure behavior. While the specific effects of stratigraphic dips and nonstationary soil orientations on slope stability are frequently analyzed, these effects on the post-failure behavior of slopes have not been thoroughly explored. This study proposes a new probabilistic framework for simulating landslides and quantifying hazard zones, incorporating complex stratigraphic dips and two typical nonstationary soil orientations. The new method integrates nonstationary random field (RF) theory with the rotation of spatial autocorrelation structure. It derives formulas for calculating the thickness and depth of the soil layer at various locations, considering different stratigraphic dips and nonstationary orientations. This approach enables the simulation of parameter distributions for bedding and inverse soils with both vertical and stratigraphic nonstationarity. The generalized interpolation material point method (GIMP) is then used to simulate the post-failure behavior of slopes. The findings indicate that neglecting the spatial variability of soil parameters leads to an underestimation of the influence zone of landslide. Additionally, the nonstationary characteristics of soil parameters and stratigraphic dips can affect the failure mechanisms of slopes and the exceedance probabilities of runout and influence distances. The proposed method enhances the accuracy of predicting runout and influence distances, serving as a novel valuable tool for disaster management and mitigation.