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Assam State Disaster Management Authority, Geological Survey of India ink MoU for landslide early warning system

Assam State Disaster Management Authority, Geological Survey of India ink MoU for landslide early warning system

The Hindu
Saturday, August 17, 2024 05:46:06 AM UTC

MoU signed to implement Experimental Regional Landslide Early Warning System in Assam for disaster prevention and mitigation

In a significant step towards combating the menace of landslides in Assam, a Memorandum of Understanding (MoU) on the Implementation of Experimental Regional Landslide Early Warning System (LEWS) was signed between Assam State Disaster Management Authority (ASDMA) and the Geological Survey of India (GSI), in Guwahati on Friday (August 17, 2024).

The MoU was signed by Gyanendra Dev Tripathi, Chief Executive Officer, ASDMA and Siladitya Sengupta, Deputy Director General of Assam, GSI. The problem of landslides sometimes goes unnoticed or individual landslides are being dealt with ad-hoc response mechanisms and the problem keeps on recurring every year.

In addition to lives lost, large numbers of houses along with infrastructure bear the brunt of the landslides in the hilly terrains of Dima Hasao, Karbi Anglong, Cachar, Karimganj, and Kamrup (M) districts in Assam.

During recent times, one of the major thrust areas in landslide risk reduction is the development of a regional Landslide Early Warning System (LEWS) for a large area with inputs from static landslide susceptibility and hazard maps; dynamic short and long-term rainfall forecasts, etc. In this context, this MoU is being signed between ASDMA and GSI with the objective of institutional cooperation and implementation of an experimental Regional Early Warning Forecast system for making an operational model on landslides for the Dima Hasao and Cachar districts of Assam. It would encompass all the State in the future. GSI informed that it has launched the "Bhooskhalan" App where people, institutions, government agencies, etc. can upload incidences of landslides in their respective areas to make it more efficient in predicting and mitigating disasters as cloud sourcing.

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