Researchers at the University of Melbourne and the Melbourne School of Engineering have developed a software tool that can predict landslide boundaries two weeks before they happen.
Professor Antoinette Tordesillas from the School of Mathematics and Statistics at the University of Melbourne and Professor Robin Batterham from the Melbourne School of Engineering, have developed a software tool that uses applied mathematics and big data analytics to predict the boundary of where a landslide will occur - two weeks in advance.
“We want as much lead time as possible to try and stop the landslide if we can, and otherwise to evacuate communities, remove equipment and prepare for recovery,” said Batterham.
“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.”
Tordesillas says 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 microstructure level – the movement of individual grains of earth,” she says.
“Of course, we cannot possibly see the movement of individual grains in a landslide or earthquake that stretches for kilometres, but 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,” adds Tordesillas.
The early clues include patterns of motion that change over time and become synchronised.
“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.
Using mining company data, usually collected by radar technologies every six minutes, the researchers are able to take the information and find the ‘hidden’ patterns.
Tordesillas explains: “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.
“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.”
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,” says Tordesillas. “The trick though is to detect the ordered motions in the network as early as possible, when differences in movements are very subtle.”
Batterham says their new algorithm is all about turning these numbers into risk assessment and management actions that can save lives.
“People have gone somewhat overboard on so-called data analytics, machine learning and so on,” he says.
“While we’ve been doing this sort of stuff for 40 years, this software harnesses the computer power and memory available to look not just at the surface movement, but extract the relevant data patterns.”