Data Science-based Separation of Triggering and Non-Triggering Rainfall of Landslides for Threshold Attribution

Territorial Landslide Early Warning Systems (Te-LEWSs) globally rely on meteorological and hydro-meteorological thresholds for effective warning dissemination. In India, the widely used Intensity-Duration (ID) curve serves as a primary meteorological threshold for landslide forecasts, while hydro-meteorological thresholds, such as the Soil-Water Index (SWI), remain under evaluation. Challenges persist in threshold attribution due to uncertainties in meteorological and landslide datasets. To address this gap, this study employs data-science-based approaches to differentiate triggering and non-triggering rainfall events across multiple Indian regions: Kerala, Maharashtra, Uttarakhand, and Himachal Pradesh. The analysis identifies ID thresholds for landslide forecasting with over 90% confidence and an accuracy exceeding 85%. Additionally, SWI-based hydro-meteorological thresholds are derived, though further refinement is needed for enhanced accuracy. Using hourly meteorological data from multiple sources, the study demonstrates the robustness of data-driven methodologies in resolving uncertainties and improving the reliability of Te-LEWS thresholds in India.