Kangra district to have landslip warning system

Lalit Mohan

Tribune News Service

Dharamsala, December 24

Kangra district would have early landslide warning system. The system was being developed by district administration Kangra and geological experts of IIT Mandi. Deputy Commissioner Kangra, Nipun Jindal, while talking to The Tribune, said that Dharamsala and Kangra regions were badly affected by the landslides. Over the years, human and tectonic activities have contributed to an increase in slope failures in the Himalayan region because of the expansion of human settlement on very fragile and highly fractured bedrock overlain by thick alluvial cover.

Suffered huge damage

• Dharamsala and Kangra had experienced many damaging landslides and rock falls which have damaged roads, houses and communication network since 1968.
• A landslide occurred in 2013 near Tirah Line locality of Dharamsala cantonment area, district and damaged more than 25 houses completely. Another Landslide at Boh village in 2021 claimed 10 human lives.

Dharamsala and Kangra had experienced many damaging landslides and rock falls which have damaged roads, houses and communication network since 1968, he said.

He further said that a landslide occurred in 2013 near Tirah Line locality of Dharamsala cantonment area, district and damaged more than 25 houses completely. This landslide occurred after continuous heavy to very heavy rainfall for four days.

Another Landslide at Boh village in 2021 claimed 10 human lives. Factors involved in the slide were steep slope, continuous infiltration of water soak pits (sewage system) located up slopes, continuous heavy to very heavy rainfall and toe erosion by local drainage. In this regard a landslide monitoring and early warning system will helpful to save human lives and properties, he said.

Jindal said that the main goal of this proposal is development and evaluation of a low-cost Landslide Monitoring Solution. In the first phase, the project would include identification of 10 most vulnerable landslide sites in the district. Micro Electromechanical Systems (MEMS) and Learning Management Systems (LMS) that would be developed locally would be installed at the selected sites.

The Learning Management Systems would then be tested on the selected sites. The project would involve development of thresholding and machine learning algorithms for monitoring landslides and predicting probability of landslide occurrence ahead of time. The evaluation of developed algorithms on Learning Management Systems and dissemination of information among different stake holders via alerts would be the next generation of the project, he said. 

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