A Probabilistic Framework for Land Deformation Prediction (Student Abstract)
Keywords:Surface Deformation, Graph Neural Network, Spatio-temporal Prediction
AbstractThe development of InSAR (satellite Interferometric Synthetic Aperture Radar) enables accurate monitoring of land surface deformations, and has led to advances of deformation forecast for preventing landslide, which is one of the severe geological disasters. Despite the unparalleled success, existing spatio-temporal models typically make predictions on static adjacency relationships, simplifying the conditional dependencies and neglecting the distributions of variables. To overcome those limitations, we propose a Distribution Aware Probabilistic Framework (DAPF), which learns manifold embeddings while maintaining the distribution of deformations. We obtain a dynamic adjacency matrix upon which we approximate the true posterior while emphasizing the spatio-temporal characteristics. Experimental results on real-world dataset validate the superior performance of our method.
How to Cite
Li, R., Zhou, F., Trajcevski, G., Zhang, K., & Zhong, T. (2022). A Probabilistic Framework for Land Deformation Prediction (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13001-13002. https://doi.org/10.1609/aaai.v36i11.21637
AAAI Student Abstract and Poster Program