Non-Structural Landslide Risk Mitigation: Schemes, Application and Case Studies

The Himalayan region encompasses nearly 12% of the total geographical area of India and exhibits a high susceptibility to landslides owing to its delicate lithology, intricate geology, and steep slopes. Over the past few decades, GIS-based hydrological, data-driven, and physical-based models have been used extensively to identify landslide-prone areas. On the other hand, not much is known regarding the reliability and applicability of these options. This paper explores rainfall thresholds, InSAR (Interferometric Synthetic Aperture Radar) analysis, and site-based monitoring in the context of rainfall-induced landslides. Hence, the article offers a holistic view of these practices. The rainfall thresholds are determined for the Mandi region which can further aid in landslide forecasting and provide an early warning if that threshold is breached. In the absence of rainfall and other ground features, InSAR analysis monitors ground deformation in real time. However, site-based monitoring methods can be employed for a specific area with a high hazard probability, as detected from susceptibility mapping. Furthermore, by integrating real-time data from subsurface systems with predictive machine learning (ML) algorithms, the monitoring system can forecast landslides and raise alerts as per significant movements. This paper offers an overview of all these techniques, focusing on real-world case studies.