Using technology to get ahead of landslides

Deforestation, spread of human population into fragile landscapes caused landslides year after year

On August 13, over 46 people died in a landslide in Himachal Pradesh. Hours later, 28 were feared killed in Pithoragarh, Uttarakhand. Earlier this year, the deaths of 152 people in Bangladesh and the countless injuries, destruction of property and loss of tourism revenues in Uttarakhand and Himachal Pradesh, can be attributed to a single natural disaster — landslides.

Every year, from June to October, the Western Ghats and the Himalayan belt across the North and Northeast  face rain-induced landslides, big and small. 

Help might finally be in sight: Scientists at Kerala’s Amrita University have developed India’s first Wireless Sensor Network, which provides accurate and early warnings of rainfall-induced landslides. Unlike existing landslide sensors, which monitor few geological variables and are not always accurate, the system developed by Amrita University analyses real-time data from multiple sensors measuring several different parameters including the rate of rainfall, the root capacity of ground cover, and the thresholds of soil saturation and instability, in order to predict when the next landslide will hit.

Here’s how it works: “We have designed a network of sensors which are spread across a landslide-prone area,” explains Maneesha Sudheer, director, Amrita Center for Wireless Networks & Applications (AmritaWNA). “Multiple parameters are recorded and the data relayed wirelessly to our centre, where specially-designed algorithms analyse it.” This system, which is one of the National Bank for Agriculture and Rural Development’s (Nabard’s) Rural Innovations awardees and has been patented in the US, employs a multidisciplinary approach that uses principles of physics, engineering, geology, computer science, and even the social sciences. Further, in keeping with the philanthropic approach of the university, Sudheer’s team focused on building relations with local stakeholders in their pilot project in Munnar, Idukki district, Kerala, funded by the Department of Electronics and Information Technology (DeitY). Not only did the team members spread awareness about the role that human interventions such as excessive construction and deforestation play in causing landslides, they found that inaccurate predictions of landslides in the past had diminished the credibility of early warning systems in the eyes of the villagers.

The site of the study, Anthoniar Colony in Munnar, where heavy rain causes landslides every year, was chosen out of 15 field areas prone to landslides, in 2008. Since that time, about 150 geophysical sensors have been connected to 25 wireless sensor nodes across seven acres. These measure rainfall, temperature, humidity, the moisture content of the soil, soil pore water pressure and more, and relay the information to Amrita University’s online software for real-time data analysis. In 2009, Sudheer’s data analytics showed an imminent threat of a landslide. The district administration, as well as the villages that were likely to be affected, was alerted. The massive landslide that occurred soon after caused minimal damage as all the villagers had been safely evacuated. “This demonstrated to us that it was possible to issue warnings as early as 24 hours before a potential landslide,” says Sudheer. “Subsequently, villagers who used to move to safer areas for the entire duration of the monsoon have been able to live their lives normally, and evacuate only when there is a warning of the direct threat of a landslide taking place.” Since then, the system accurately predicted landslides in 2011 and 2013. Operational for nearly 10 years now, it serves as an example of how early warning systems can transform the lives of people living in landslide-prone areas.

Sudheer’s Munnar study has caught the attention of international research bodies as well as other state governments. The International Programme on Landslides (IPL) has declared AmritaWNA as the “World Centre of Excellence on Landslide Disaster Reduction”. The North-East Council has funded Sudheer’s team to perform a similar pilot project in Sikkim, using 10 sensors. “The project will be completed by next monsoon,” she says. Maharashtra, which has identified 111 landslide-prone sites, is also studying the Munnar case to see if the sensors can be replicated effectively there. Sudheer is confident that her system will work across diverse geological regions, as the parameters affecting rainfall-induced landslides can be tailored to suit each region. With an estimated 12 per cent of the country’s land area classified as being at risk from landslides, the scope of this wireless sensor is immense. “We hope that it will be replicated in landslide-prone areas of the Darjeeling Hills in north Bengal and elsewhere in the country like the Northeast, Uttarakhand, Himachal Pradesh, and Kashmir,” says she.

Two major challenges lie ahead. “Before we implement the landslide sensors across several different sites, we want to amass and analyse as much data as possible to refine the technology,” says Sudheer. The second challenge is to bring down the cost of the system (the Munnar project cost Rs 5 crore) by reducing the cost of equipment. The scientist believes that wireless sensors, such as the ones they have used to develop the Wireless Sensor Network System for Landslide Detection, have tremendous potential for more accurate environmental monitoring and disaster management. Not only are they small and inexpensive and can be connected to a network without using cables, they can be used to collect data, which, when analysed, can eventually refine the complex task of forecasting many other natural disasters, not just landslides. To this end, Sudheer and her team are working on a European Union-funded project on Wireless Sensor Networks with Self Organisation (WINSOC). The idea is to develop systems of sensors that mimic biological systems that can interact locally instead of doing so in a distant data fusion centre. These local interactions produce estimates that are more accurate than those provided by a single sensors, and result in greater accuracy and reliability of the entire sensor network. These have implications for gathering data not just on landslides, but also on air, soil and water quality levels, and much more. “It’s challenging, but at the end of the day, a source of great satisfaction that our scientific research is helping improve people’s quality of life,” says Sudheer. Indeed, even though this is the peak of the monsoon in Munnar, it is business as usual for the residents of Anthoniar Colony. They know that their sensor will warn them before another landslide strikes.