Researchers have developed a new way to predict landslide displacement using satellite images and machine learning, offering a cost-effective solution to this crucial issue.
According to a recent study published in the journal Engineering Geology, a team of researchers from various institutions in China and Germany collaborated on a novel framework for landslide displacement prediction. The traditional method of using geotechnical in-situ monitoring for displacement prediction has proven effective but comes with high costs and spatial limitations, making it impractical for widespread use in large areas.
The researchers utilized Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) along with machine learning techniques to analyze satellite images and extract displacement time series data for landslides. This approach allows for the monitoring of landslide deformation in a more cost-effective manner, enabling early warning systems and forecasting efforts.
In a practical application of this new prediction method in the Three Gorges Reservoir area in China, the researchers found that MT-InSAR accurately tracked landslide deformation, while machine learning algorithms established a precise relationship between the deformation and its triggers.
By combining the strengths of MT-InSAR and machine learning, the researchers developed a prediction framework that takes into account the physics principles behind landslide deformation. This framework can effectively predict landslide displacement within large areas at a reduced cost, providing valuable insights for early warning systems and disaster preparedness efforts.
The study underscores the importance of leveraging innovative technologies like satellite imaging and machine learning to address pressing geological challenges such as landslide prediction. This new framework has the potential to revolutionize the way we approach landslide displacement forecasting, offering a more efficient and cost-effective solution for at-risk areas around the world.
