A Morphology-Guided Conditional Generative Adversarial Network for Rapid Prediction of Hazard Gas Dispersion Field in Complex Urban Environments

urban district in China. The results demonstrate that under varying wind speeds (0.5-1.5 m/s) and temporal scales (5-60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments.