Scalable Variational Learning for Noisy-OR Bayesian Networks with Normalizing Flows for Complex Cascading Disaster Systems

Sudden-onset disasters such as earthquakes often induce multiple cascading hazards and impacts, causing human and economic losses. While recent advances in remote sensing technologies provide valuable data for rapid hazard assessment, current methods face two fundamental limitations. First, the inherent causal relationship and co-location of disaster-induced hazards and impacts makes the decoupling of individual hazard or impact particularly challenging. Second, and critically for disaster response, existing methods cannot adapt to new information during the critical response period - they provide only static initial estimates despite the continuous influx of ground truth data from field reconnaissance. This inability to update and refine assessments in real-time severely limits their practical utility for emergency response, where understanding of the disaster's impact typically evolves significantly over time. Herein, we present online-DisasterVINF, a framework that uniquely addresses both limitations. We leverage Noisy-OR Bayesian networks, which are particularly suited for modeling how multiple hazards independently contribute to the observed impacts, combined with normalizing flows to provide a more expressive alternative to simple log-linear relationships. The framework is also capable of improving its estimation by incorporating ground truth data as it becomes available through post-disaster reconnaissance. We develop a novel variational inference approach that jointly approximates posteriors by leveraging complex causal relationships and remote sensing techniques. We evaluate our framework on multiple seismic events from diverse countries around the globe. Our results demonstrate that online-DisasterVINF significantly enhances estimation accuracy compared to existing methods, while our analysis shows the online updating mechanism substantially improves model performance as ground truth becomes available, underlining its adaptability for real-time disaster response.