Slope Instability Predictor-Kerala (SLIP-K): A mobile/web Application for Landslide Hazard Prediction in Idukki, India

Document Type

Article

Publication Date

1-1-2025

Department

College of Engineering; Department of Geological and Mining Engineering and Sciences

Abstract

The Western Ghats region of Idukki district in southern India is highly susceptible to rainfall-induced landslides due to steep topography, intense monsoons, and increasing land-use pressures. To address the need for localized landslide early warning system (LEWS), we developed the Slope Instability Predictor–Kerala (SLIP-K), a real-time system that integrates a physics-based landslide susceptibility model (Geographic Information System-Tool for Infinite Slope Stability Analysis (GIS-TISSA)) with empirical rainfall thresholds (RTs) quantified through data from eight automated weather stations (AWS). SLIP-K operates through an interactive web/mobile application, delivering 15-minute interval risk updates and user-friendly alerts to communities using Google Earth Engine-driven geospatial mapping. Beyond inventory-based or statistical models, SLIP-K offers physically interpretable outputs, community participatory reporting, and multilingual support. A unique aspect of this study is the multi-year, quantitative validation of SLIP-K using AWS data and fatal landslide inventories (2021–2024). Confusion matrix analysis across all AWS sites demonstrated high sensitivity (recall = 1.00), robust accuracy (0.91), and successful identification of all recorded fatal events. Additional assessments yielded a red alert success rate of 33.3% and an Area Under the Curve (AUC) of 0.82–0.88, comparable to national and international benchmarks. These findings establish SLIP-K as a transparent, statistically robust, and scalable landslide early warning framework, supporting risk reduction strategies across data-limited and topographically complex mountain environments.

Publisher's Statement

© 2025 Springer Nature

Publication Title

Earth Systems and Environment

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