The global increase in landslide occurrences, impacting millions and resulting in casualties, underscores the urgent need for robust and cost-effective early warning systems and emergency response efforts. Recent advancements in landslide early warning systems have been promising, yet the complexities of larger, dynamic, and cascading hazards demand a more comprehensive approach. This project proposes the Catchment Scale Landslide Monitoring and Early Warning System CatchMEWS to be piloted in the upper catchments of the Periyar river in Kerala’s Western Ghats. The semi-deterministic dynamic model integrates geological and real-time metro-hydrological data to understand landslide risk stages from initiation to impact. Through comprehensive investigation of causative factors and variations in triggering factors, this work aims to enhance understanding of slope instability processes. The methodology integrates local perspectives and community-centric approaches, developing a validated database of landslide events and fostering sustainable development through geospatial and machine learning techniques. This project emphasizes the importance of flexible strategies in geological hazard management, offering insights to accurately assess and predict landslide risks, develop sustainable models, and address similar challenges globally.
Amrita Team Members
Name of the Indian Collaborators
Name of the International Collaborators: Guido Zolezzi, Professor – University of Trento – Italy, Dept. of Civil Environmental and Mechanical Engineering, Center Agriculture Food Environment, Chairholder – UNESCO Chair in Engineering for Human and Sustainable Development, Rector’s Delegate for Cooperation and Development Coordinator of CUCS – Italian University Network for Development Cooperation
Name of the Industry Collaborators : ESRI
Product Details
Proposed Future Work Details: Implementation into various other catchments for Disaster risk Reduction
