Gas leakage poses a significant hazard in chemical industry operations, where failure to respond rapidly to gas diffusion can lead to poisoning, fire, or explosion. Timely and accurate prediction of gas dispersion is therefore essential for emergency decision-making and operational safety. While existing methods such as computational fluid dynamics, spatiotemporal statistics, and surrogate models emphasize prediction accuracy, they often suffer from excessive computational delays—especially critical in leak scenarios where casualties can occur within minutes. To address this gap, this paper introduces a Gaussian process-Markov random field-Kriging (GP-MRF-K) model for fast and reliable pre... More >