Towards physics-informed neural networks for landslide prediction

TL;DR

This paper introduces a physics-informed neural network approach for landslide prediction that integrates geotechnical physics with data-driven models, enabling regional-scale susceptibility mapping and potential near-real-time forecasts.

Contribution

It develops a novel PINN architecture that retrieves geotechnical parameters from proxy variables and improves landslide susceptibility prediction at large scales.

Findings

High predictive accuracy in landslide susceptibility mapping

Generation of regional geotechnical property maps

Potential for near-real-time landslide prediction

Abstract

For decades, solutions to regional scale landslide prediction have mostly relied on data-driven models, by definition, 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 to a standard data-driven architecture, an intermediate constraint 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 minimize a loss function with respect to the available coseismic landside inventory. The results are very 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. Such architecture is therefore framed to tackle coseismic landslide prediction, something that, if confirmed in other studies, could open up towards PINN-based near-real-time predictions.