Towards physics-informed neural networks for landslide prediction

For decades, solutions to regional-scale landslide prediction have primarily relied on data-driven models, which, by definition, are disconnected from the physics of the failure mechanism. The success and spread of such tools came from the ability to exploit proxy variables rather than explicit geotechnical ones, as the latter are prohibitive to acquire over broad landscapes. Our work implements a Physics Informed Neural Network (PINN) approach, thereby adding an intermediate constraint to a standard data-driven architecture to solve for the permanent deformation typical of Newmark slope stability methods. This translates into a neural network tasked with explicitly retrieving geotechnical parameters from common proxy variables and then minimizing a loss function with respect to the available coseismic landslide inventory. The results are promising because our model not only produces excellent predictive performance in the form of standard susceptibility output but, in the process, also generates maps of the expected geotechnical properties at a regional scale. Therefore, Such architecture is framed to tackle coseismic landslide prediction, which, if confirmed in other studies, could open up PINN-based near-real-time predictions.