国際会議 · 2025
No Labels, No Problem: Adaptive Disaster Prediction Using Physics-Hybrid AI.
Abstract
Due to global climate change, developing robust and accurate disaster prediction systems has become essential worldwide, as severe disasters are increasingly occurring even in previously safe regions.Two key technical challenges in disaster prediction are data scarcity and regional heterogeneity.Disasters are rare and extreme, resulting in limited data, which hinders data-driven approaches like machine learning (ML).Moreover, hydro-meteorological disasters depend on region-specific factors such as rainfall patterns, soil, vegetation, and infrastructure.These, combined with dynamic environmental changes from earthquakes, climate, and urbanization, complicate the generalization of models across time and space.We propose a self-adaptive physics-hybrid disaster prediction system that autonomously adapts to diverse and evolving environments to address this.Our system consists of: (1) a physics-hybrid disaster prediction model integrating physics-based hydro-meteorological simulation and multi-modal ML models, and (2) an environmental adaptation utilizing test-time adaptation (TTA) to update parameters without labeled disaster data(i.e., disaster event data).Our physics-hybrid model is anchored in the physics model to balance stability and adaptability, leveraging strengths from both physics and ML models.Furthermore, the strong inductive bias of the physics model mitigates overfitting and catastrophic forgetting during TTA, enabling robust adaptation to unseen regions.
