N-BEATS deep learning method for landslide deformation monitoring and prediction based on InSAR: a case study of Xinpu landslide

Landslides usually occur suddenly and cause great damage, often causing serious life safety accidents and property losses. The monitoring and prediction methods of landslide deformation with high reliability, high precision and anti-difference performance are of practical significance to the needs of national disaster prevention and mitigation. Interferometric synthetic aperture radar(InSAR) technology is a monitoring method capable of all-day and all-weather observation, obtaining images with high spatial resolution and wide coverage, and capturing dynamic changes of spatio-temporal dimensions with high sensitivity. However, at present, the landslide prediction based on InSAR time series image is very rare. This paper presents a landslide prediction method based on deep learning, which can effectively solve the problem of medium- and short-term landslide prediction by exploiting multi-temporal InSAR observations. Neural basis expansion analysis (N-BEATS) network model was used to predict the landslide in the Xinpu area, the Three Gorges. The landslide prediction was completed with an accuracy (root mean square error) of 1.1 mm. The results are analyzed by the regularity of data structure, comparison to traditional methods, evaluation of the tolerance and estimation of the confidence interval. The results show that the proposed prediction method has outstanding advantages of high precision, high reliability and certain robust estimation ability.