The Union Cabinet has approved the proposed Memorandum of Understanding (MoU) between Geological Survey of India (GSI) and The Research Institute for Geo-Hydrological Protection of the National Research Council (CNR-IRPI), Italy for collaboration in Landslide Forecast and Early Warning.
The PIB reported that this partnership aims to enrich the knowledge and acquire skills in landslide forecasting and early warning through collaborative research. It is crucial to enhance and scale up India’s Landslide Early Warning Systems (LEWS) to international standards, enabling better prediction and mitigation of landslide-related damages, and to overcome the situation like Wayanad Landslide in Kerala.
This collaboration will help integrate GSI’s landslide inventory, susceptibility, and forecast maps with PM Gati Shakti, enhancing resilience and reducing vulnerability in fragile hilly terrains. The LEWS will act as a critical tool to secure lives and prevent destruction in hill areas, facilitating sustainable development and environmental protection. Plans are in place to expand the forecasting system to include more districts, with improved spatial and temporal resolution, across highly landslide-prone regions.
GSI has established a state-of-the-art National Landslide Forecasting (NLFC) facility at GSI, Kolkata to develop and operationalize the regional Landslide Early Warning Systems (LEWS) in all the landslide prone States/ UTs in India within a planned timeline to reduce landslide risks in India. The vision of NLFC is to provide timely information on landslide forecasting to the stakeholders and to the community for preparedness and minimising the landslide risks. Presently, NLFC is providing live forecast reporting on daily basis in Kalimpong & Darjeeling districts of West Bengal and Nilgiri district of Tamil Nadu. Along with that, ground testing of bulletin is in progress in 13 districts.
The Research Institute for Geo-Hydrological Protection (CNR-IRPI), Italy, is a leading institution in developing regional LEWS models, featuring granular spatial and temporal resolutions, including nowcasting capabilities.
