An Open-Data-Driven Enhanced Bayesian Decision Network for System-Level UAV Accident-Severity Analysis and Response Simulation

Unmanned aerial vehicle (UAV) accidents pose growing challenges to public safety and airspace management. This study develops an open-data-driven hierarchical Bayesian network (BN) with a Leaky Noisy-OR mechanism to analyse factors associated with consequence severity among recorded UAV accidents, and extends it to a Bayesian decision network for response simulation. Using 633 public accident records and matched meteorological data, 14 binary risk-factor nodes spanning human, machine, environmental, and management dimensions were constructed. Stratified five-fold cross-validation yielded a mean validation F1 score of 0.922 and an AUC of 0.784. Backward inference ranked airspace exposure, wind, and operation error highest under severe-consequence conditioning, whereas sensitivity analysis identified wind, bad weather history, and operation error as the most influential root-node parameters. Under the assumed directed acyclic graph (DAG), the bad weather history→weather→environment→risk state path had the highest average edge-influence score (0.853). Under the baseline safety-priority assumptions, the reroute strategy was preferred, yielding the highest expected utility (31.967) and reducing the model-estimated post-decision high-risk probability from 81% to 45%. Alternative preference settings ranked the adjust strategy first. The framework integrates open-data severity analysis with assumption-explicit response simulation.