Slow-Moving Landslides Modelling using PhysicsInformed Neural Networks
DOI:
https://doi.org/10.64862/Keywords:
Landslides, Deep Learning, InSAR, PINNAbstract
Slow moving Landslide dynamics involve complex interactions between geological, hydrological, and mechanical processes that are challenging to model using conventional methods. Physics-Informed Neural Networks (PINNs) provide a promising alternative by embedding governing physical laws into neural network training. This approach allows for the efficient capture of landslide deformation patterns and kinematic evolution, even with sparse observational data. By constraining the learning process with partial differential equations, PINNs ensure physically consistent outputs while maintaining model interpretability. Their ability to incorporate domain knowledge makes them well-suited for modeling slow-moving landslides, offering potential for real-time forecasting and enhancing early warning systems in data-limited environments.
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