Beyond Data Scarcity: A Physics-Integrated Landslide Prediction for Urban Disaster Resilience

Research Note

Climate change has increased the frequency and intensity of extreme rainfall, causing landslides even in regions without historical disaster records. Accurate prediction of such events in urban environments faces two fundamental obstacles: (i) limited observability — many physical variables that directly govern slope stability, such as subsurface water content and geotechnical properties, cannot be measured at scale; and (ii) strong regional heterogeneity, where disaster data are inherently non-IID across regions because of differences in rainfall, terrain, vegetation, and urban structure.

We propose a physics-integrated deep learning system that addresses both challenges. A three-layer tank model, incorporated as a physics-based module, supplies intermediate hydrological state as input features and is coupled to the learned model through a skip connection that adaptively controls its contribution under abnormal rainfall. In addition, Stochastic Feature Augmentation is applied during training to improve robustness against regional distribution shifts.

Experiments across 19 regions in Japan show that the proposed system achieves superior PR-AUC and generalization, even on previously unseen regions, compared with state-of-the-art baselines. The results demonstrate the effectiveness of co-designing physics knowledge with deep learning for disaster prediction tasks where data are inherently scarce and imbalanced.

We are extending this direction toward urban disaster resilience, including distributed learning and tighter integration with meteorological and geological observation infrastructure.