New software may predict landslides weeks in advance

MELBOURNE: Australian scientists have developed a new software that may help predict landslides two weeks before they actually happen, and potentially save lives.

Landslides - masses of rock, earth or debris moving down a slope - can affect communities and the economy, or take lives, said researchers at the University of Melbourne in Australia.

A recent landslide at a jade mine in Myanmar, for example, claimed at least 27 lives, they said.

The new software uses applied mathematics and big data analytics to predict the boundary of where a landslide will occur.

"We can now predict when a rubbish landfill might break in a developing country, when a building will crack or the foundation will move, when a dam could break or a mudslide occur. This software could really make a difference," said Robin Batterham from the Melbourne School of Engineering.

According to Antoinette Tordesillas, a professor at the University of Melbourne, there are always warning signs in the lead up to a collapse or 'failure', the tricky part is identifying what they are.

"These warnings can be subtle. Identifying them requires fundamental knowledge of failure at the micro- structure level - the movement of individual grains of earth," she said. "If we can identify the properties that characterise failure in the small-scale, we can shed light on how failure evolves in time, no matter the size of the area we are observing," Tordesillas said.

These early clues include patterns of motion that change over time and become synchronised, researchers said.

"In the beginning, the movement is highly disordered. But as we get closer to the point of failure - the collapse of a sand castle, a crack in the pavement or a slip in an open pit mine - motion becomes ordered as different locations suddenly move in similar ways," said Tordesillas.

Currently mining companies use radar technologies to produce data, every six minutes, on the surface movement of slopes at very high resolution - sub-millimetre precision.

"We take this information and turn the numbers into a network that allows us to extract the hidden patterns on motion and how they are changing in space and time," said Tordesillas.

"First, we need to decide which dots, that is locations on the surface of the mountain or mine, are moving. For each pair of dots, we ask whether their surface movements are similar. If so, the dots are linked. We do this for every pair of dots until we get a network," she said.

The stable locations will barely move, while unstable areas will move quite a lot.

"As we get closer and closer to failure, this pattern of division in movement is quite clear in the network," said Tordesil

las. There is an overwhelming amount of data available in risk monitoring platforms for natural hazards like

landslides.