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

Abstract

Global climate change has increased the frequency and intensity of extreme rainfall events, causing landslides even in regions with little or no historical disaster records. Accurately predicting such events in urban environments requires integrating heterogeneous environmental data within a system-level co-design framework. However, two key challenges hinder reliable prediction: (i) limited observability due to scarce disaster records and the difficulty of measuring essential physical variables such as subsurface hydrology, and (ii) strong regional heterogeneity, where disaster data exhibit inherently non-IID characteristics across regions. Traditional physics-based models often rely on simplified assumptions because observable variables are limited, whereas deep learning models tend to overfit under scarce and imbalanced data. To address these issues, this study proposes a physics-integrated deep learning system that combines a three-layer tank model as a physics-based model with a data-driven deep learning model. The physics-based outputs are incorporated both as input features and through a skip-connection that adaptively controls their contribution under abnormal rainfall conditions. In addition, Stochastic Feature Augmentation is employed to improve robustness against regional data distribution shifts. Experiments conducted across 19 regions in Japan demonstrate that the proposed system achieves improved generalization and superior PR-AUC in even unseen regions compared with state-of-the-art methods.